Machine Learning and data mining is part SCIENCE (ML algorithms, optimization), part ENGINEERING (large-scale modelling, real-time decisions), part PROCESS (data understanding, feature engineering, modelling, evaluation, and deployment), and part ART. In this talk, Dr. Shailesh Kumar focuses on the "ART of data mining" - the little things that make the big difference in the quality and sophistication of machine learning models we build. Using real-world analytics problems from a variety of domains, Shailesh shares a number of practical learnings in: (1) The art of understanding the data better - (e.g. visualization of text data in a semantic space) (2) The art of feature engineering - (e.g. converting raw inputs into meaningful and discriminative features) (3) The art of dealing with nuances in class labels - (e.g. creating, sampling, and cleaning up class labels) (4) The art of combining labeled and unlabelled data - (e.g. semi-supervised and active learning) (5) The art of decomposing a complex modelling problem into simpler ones - (e.g. divide and conquer) (6) The art of using textual features with structured features to build models, etc. The key objective of the talk is to share some of the learnings that might come in handy while "designing" and "debugging" machine learning solutions and to give a fresh perspective on why data mining is still mostly an ART.
Views: 1989 HasGeek TV
The CURE (Clustering Using Representatives) Algorithm is large scale clustering algorithm in the point assignment class which assumes Euclidean space. It does not assume anything about the shape of clusters; they need not be normally distributed, and can even have strange bends, S-shapes, or even rings. #RanjiRaj #BigData #CURE Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj
Views: 5694 Ranji Raj
There is much information to be gained by analyzing the large-scale data that is derived from social networks. The best-known example of a social network is the “friends” relation found on sites like Facebook. However, as we shall see there are many other sources of data that connect people or other entities. #RanjiRaj #BigData #SocialNetworkGraph Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj ويستند هذا الفيديو على مفاهيم مثل الحافة بينغريس وجريفان نيومان خوارزمية في الرسوم البيانية الاجتماعية Este video se basa en conceptos como Edge entreess y el algoritmo de Grivan Newman en los gráficos sociales Это видео основано на таких понятиях, как Edge interess и Grivan Newman Algorithm в социальных графах Cette vidéo est basée sur des concepts tels que interess et Girvan bord Newman algorithme dans les graphiques sociaux Dieses Video basiert auf Konzepten wie Edge zwischeness und Grivan-Newman Algorithmus in den sozialen Graphen Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter 👉https://twitter.com/iamRanjiRaj Like TheStudyBeast on Facebook 👉https://www.facebook.com/thestudybeast/ For more videos LIKE SHARE SUBSCRIBE
Views: 3660 Ranji Raj
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Views: 29590 Artificial Intelligence - All in One
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 94372 MIT OpenCourseWare
http://www.bigdataspain.org Abstract: http://www.bigdataspain.org/2014/conference/large-scale-graphs-with-google-tm-pregel This talk will give a good overview over the complex architecture of the Pregel framework and will give some insights where there are potential bottlenecks when writing a Pregel algorithm. Session presented at Big Data Spain 2014 Conference 17th Nov 2014 Kinépolis Madrid Event promoted by: http://www.paradigmatecnologico.com Slides: https://speakerdeck.com/bigdataspain/processing-large-scale-graphs-with-google-tm-pregel-by-michael-hackstein-at-big-data-spain-2014
Views: 5539 Big Things Conference
Data mining concepts Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.Data mining Data mining involves six common classes of tasks: Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam". Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets. Summarization – providing a more compact representation of the data set, including visualization and report generation.
Views: 641 Technology mart
Machine Learning #74 CURE Algorithm | Clustering In this lecture of macghine learning we are going to see CURE Algorithm for clustering with example. A new scalable algorithm called CURE is introduced, which uses random sampling and partitioning to reliably find clusters of arbitrary shape and size. CURE algorithm clusters a random sample of the database in an agglomerative fashion, dynamically updating a constant number c of well-scattered points. CURE divides the random sample into partitions which are pre-clustered independently, then the partially-clustered sample is clustered further by the agglomerative algorithm. A new algorithm for detecting arbitrarily-shaped clusters at large-scale is presented and named CURE, for “Clustering Using Representatives”. Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 952 Xoviabcs
Author: Evangelos Papalexakis, Department of Computer Science and Engineering, University of California, Riverside Abstract: What does a person’s brain activity look like when they read the word apple? How does it differ from the activity of the same (or even a different person) when reading about an airplane? How can we identify parts of the human brain that are active for different semantic concepts? On a seemingly unrelated setting, how can we model and mine the knowledge on web (e.g., subject-verb-object triplets), in order to find hidden emerging patterns? Our proposed answer to both problems (and many more) is through bridging signal processing and large-scale multi-aspect data mining. Specifically, language in the brain, along with many other real-word processes and phenomena, have different aspects, such as the various semantic stimuli of the brain activity (apple or airplane), the particular person whose activity we analyze, and the measurement technique. In the above example, the brain regions with high activation for “apple” will likely differ from the ones for “airplane”. Nevertheless, each aspect of the activity is a signal of the same underlying physical phenomenon: language understanding in the human brain. Taking into account all aspects of brain activity results in more accurate models that can drive scientific discovery (e.g, identifying semantically coherent brain regions). In addition to the above Neurosemantics application, multi-aspect data appear in numerous scenarios such as mining knowledge on the web, where different aspects in the data include entities in a knowledge base and the links between them or search engine results for those entities, and multi-aspect graph mining, with the example of multi-view social networks, where we observe social interactions of people under different means of communication, and we use all aspects of the communication to extract communities more accurately. The main thesis of our work is that many real-world problems, such as the aforementioned, benefit from jointly modeling and analyzing the multi-aspect data associated with the underlying phenomenon we seek to uncover. In this thesis we develop scalable and interpretable algorithms for mining big multiaspect data, with emphasis on tensor decomposition. We present algorithmic advances on scaling up and parallelizing tensor decomposition and assessing the quality of its results, that have enabled the analysis of multi-aspect data that the state-of-the-art could not support. Indicatively, our proposed methods speed up the state-of-the-art by up to two orders of magnitude, and are able to assess the quality for 100 times larger tensors. Furthermore, we present results on multi-aspect data applications focusing on Neurosemantics and Social Networks and the Web, demonstrating the effectiveness of multiaspect modeling and mining. We conclude with our future vision on bridging Signal Processing and Data Science for real-world applications. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 142 KDD2017 video
Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: https://www.jpinfotech.org Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy of any single transaction. Current solutions for this problem cannot well balance efficiency, privacy, and data utility over large-scale data. Toward this end, we propose an efficient, differential private frequent itemsets mining algorithm over large-scale data. Based on the ideas of sampling and transaction truncation using length constraints, our algorithm reduces the computation intensity, reduces mining sensitivity, and thus improves data utility given a fixed privacy budget. Experimental results show that our algorithm achieves better performance than prior approaches on multiple datasets.
Views: 56 jpinfotechprojects
SF Bay Area ACM Data Mining SIG http://www.sfbayacm.org/?p=1265 Location: LinkedIn, 2027 Stierlin Ct., Mountain View, CA 94043. Notice: NEW MEETING LOCATION for 2010 Date: Monday Feb 22, 2010; 6:30 pm Cost: Free and open to all who wish to attend, but membership is only $20/year. Anyone may join our mailing list at no charge, and receive announcements of upcoming events. Speaker: Michael W. Mahoney, Stanford University TITLE: "Algorithmic and Statistical Perspectives on Large-Scale Data Analysis" DESCRIPTION: Computer scientists and statisticians have historically adopted quite different views on data and thus on data analysis. In recent years, however, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are also useful in practice for solving large-scale scientific and Internet data analysis problems. After reviewing these two complementary perspectives on data, I will describe two recent examples of improved algorithms that used ideas from both areas in novel ways. The first example has to do with improved methods for structure identification from large-scale DNA SNP data, a problem which can be viewed as trying to find good columns or features from a large data matrix. The second example has to do with selecting good clusters or communities from a data graph, or demonstrating that there are none, a problem that has wide application in the analysis of social and information networks. Understanding how statistical ideas are useful for obtaining improved algorithms in these two applications may serve as a model for exploiting complementary algorithmic and statistical perspectives in order to solve applied large-scale scientific and Internet data analysis problems more generally. SPEAKER BIOGRAPHY Dr. Mahoney is currently at Stanford University. His research interests focus on theoretical and applied aspects of algorithms for large-scale data problems in scientific and Internet applications. Currently, he is working on geometric network analysis; developing approximate computation and regularization methods for large informatics graphs; and applications to community detection, clustering, and information dynamics in large social and information networks. In the past, he has worked on randomized matrix algorithms and applications in genetics and medical imaging. He has been a faculty member at Yale University and a researcher at Yahoo Research, and his PhD was is computational statistical mechanics at Yale University. See also http://cs.stanford.edu/people/mmahoney/ Also he is involved in running the MMDS 2010 meeting on June 15-18, 2010. See details up at the web page http://mmds.stanford.edu/ soon, or details of prior year's Workshop on Algorithms for Modern Massive Data Sets. Michael Mahoney
Views: 2400 San Francisco Bay ACM
Clustering is often an essential first step in datamining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can offer a richer representation by suggesting the potential group structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of single-linkage clustering algorithm due to its natural expression of iterative process. Our algorithm can be deployed easily in Amazon’s cloud environment. And a thorough performance evaluation in Amazon’s EC2 verifies that the scalability of our algorithm sustains when the datasets scale up.
Views: 2389 Spark Summit
Large Scale Hierarchical Classification: Foundations, Algorithms and Applications Part 1 Author: Huzefa Rangwala, George Mason University Abstract: Massive amount of available data in various forms such as text, image, and videos has mandated the need to provide a structured and organized view of the data to make it usable for data exploration and analysis. Hierarchical structure/taxonomies provides a natural and convenient way to organize information. Data organization using hierarchy has been extensively used in several domains - gene taxonomy for organizing gene sequences, DMOZ taxonomy for webpages, International patent classification hierarchy for browsing patent documents and ImageNet for indexing millions of images. Given, a hierarchy containing thousands of classes (or categories) and millions of instances (or examples), there is an essential need to develop an efficient and automated approaches to categorize unknown instances. This problem is referred to as Hierarchical Classification (HC) task. HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this tutorial, we will cover technical material related to large scale hierarchical classification. This will be meant for an audience with intermediate expertise in data mining having a background in classification (supervised learning). Formal definitions of hierarchical classification and variants will be discovered, along with a brief discussion on structured learning. Link to tutorial: http://cs.gmu.edu/~mlbio/kdd2017tutorial.html More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 893 KDD2017 video
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) but is also generalized to any kind of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In the proper use of the word, the key term is discovery, commonly defined as "detecting something new". Even the popular book "Data mining: Practical machine learning tools and techniques with Java"(which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.
Views: 52549 John Paul
By Dorit Simona Hochbaum. The dominant algorithms for machine learning tasks fall most often in the realm of AI or continuous optimization of intractable problems. This tutorial presents combinatorial algorithms for machine learning, data mining, and image segmentation that, unlike the majority of existing machine learning methods, utilize pairwise similarities. These algorithms are efficient and reduce the classification problem to a network flow problem on a graph. One of these algorithms addresses the problem of finding a cluster that is as dissimilar as possible from the complement, while having as much similarity as possible within the cluster. These two objectives are combined either as a ratio or with linear weights. This problem is a variant of normalized cut, which is intractable. The problem and the polynomial-time algorithm solving it are called HNC. It is demonstrated here, via an extensive empirical study, that incorporating the use of pairwise similarities improves accuracy of classification and clustering. However, a drawback of the use of similarities is the quadratic rate of growth in the size of the data. A methodology called “sparse computation” has been devised to address and eliminate this quadratic growth. It is demonstrated that the technique of “sparse computation” enables the scalability of similarity-based algorithms to very large-scale data sets while maintaining high levels of accuracy. We demonstrate several applications of variants of HNC for data mining, medical imaging, and image segmentation tasks, including a recent one in which HNC is among the top performing methods in a benchmark for cell identification in calcium imaging movies for neuroscience brain research.
Views: 171 INFORMS
Match the applications to the theorems: (i) Find the variance of traffic volumes in a large network presented as streaming data. (ii) Estimate failure probabilities in a complex systems with many parts. (iii) Group customers into clusters based on what they bought. (a) Projecting high dimensional space to a random low dimensional space scales each vector's length by (roughly) the same factor. (b) A random walk in a high dimensional convex set converges rather fast. (c) Given data points, we can find their best-fit subspace fast. While the theorems are precise, the talk will deal with applications at a high level. Other theorems/applications may be discussed.
Views: 2625 Microsoft Research
✅ Algorithms and Data Structures Masterclass: http://bit.ly/algorithms-masterclass-java ✅ FREE Java Programming Course: http://bit.ly/first-steps-java ✅ FREE Top Programming Interview Questions: http://bit.ly/top-programming-intervi... ✅ Full Numerical Methods Course: http://bit.ly/numerical-methods-java ✅ Find more: https://www.globalsoftwaresupport.com/ ===================================================== In this course we are going to consider the most relevant numerical methods that are being used on a daily basis. We'll implement the algorithms in Java ✘ matrix operations ✘ how to calculate the inverse of a matrix (Gauss-elimination) ✘ numerical integration ✘ solving differential equations ✘ Euler's method and Runge-Kutta method ===================================================== ✅ Instagram: https://www.instagram.com/global.software.algorithms/ ✅ Facebook: https://www.facebook.com/Global-Software-Support-2420513901306285/
Views: 82912 Balazs Holczer
Authors: Carlos H. C. Teixeira, Alexandre J. Fonseca, Marco Serafini, Georgos Siganos, Mohammed J. Zaki, Ashraf Aboulnaga Abstracts: Distributed data processing platforms such as MapReduce and Pregel have substantially simplified the design and deployment of certain classes of distributed graph analytics algorithms. However, these platforms do not represent a good match for distributed graph mining problems, as for example finding frequent subgraphs in a graph. Given an input graph, these problems require exploring a very large number of subgraphs and finding patterns that match some "interestingness" criteria desired by the user. These algorithms are very important for areas such as social networks, semantic web, and bioinformatics. In this paper, we present Arabesque, the first distributed data processing platform for implementing graph mining algorithms. Arabesque automates the process of exploring a very large number of subgraphs. It defines a high-level filter-process computational model that simplifies the development of scalable graph mining algorithms: Arabesque explores subgraphs and passes them to the application, which must simply compute outputs and decide whether the subgraph should be further extended. We use Arabesque's API to produce distributed solutions to three fundamental graph mining problems: frequent subgraph mining, counting motifs, and finding cliques. Our implementations require a handful of lines of code, scale to trillions of subgraphs, and represent in some cases the first available distributed solutions. ACM DL: http://dl.acm.org/citation.cfm?id=2815400.2815410 DOI: http://dx.doi.org/10.1145/2815400.2815410
Views: 1780 Association for Computing Machinery (ACM)
Provides steps for applying Image classification & recognition using CNN with easy to follow example. CNN is considered 'gold standard' for large scale image classification. R file: https://goo.gl/trgsuH Data: https://goo.gl/JmEjmc Machine Learning videos: https://goo.gl/WHHqWP Uses TensorFlow (by Google) as backend for CNN and includes, - Advantages - layers - parameter calculations - load keras and EBImage packages - read images - explore images and image data - resize and reshape images - one hot encoding - sequential model - compile model - fit model - evaluate model - prediction - confusion matrix large scale Image Classification & Recognition using cnn with Keras is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 11447 Bharatendra Rai
2018 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2018 and 2019 IEEE [email protected] TMKS Infotech,Bangalore
Views: 429 manju nath
Running data mining algorithm for finding frequent items from large data on AWS Cloud
Views: 70 Sanket Thakare
Machine Learning is rapidly becoming ubiquitous in computation, from deep learning for images, speech and language to large-scale data mining and decision support. But some major challenges remain including: 1) how to cope with sparse expert answers (labels) to train accurate models, 2) how to explain the learned behaviors, 3) how to combine knowledge and constraints with data, especially when the latter is scarce. The presentation will introduce proactive learning from multiple sources, transfer/multi-task learning, and address issues in application of these methods to different areas, such as natural language processing and computational biology
Views: 178 ITAM
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 8257 The Audiopedia
The present thesis aims to test the viability of the integration of machine learning capabilities into web map servers. The validation of this hypothesis has been carried out by the development of a pre-operational prototype. The developed prototype is a platform for thematic mapping by supervised learning from very high resolution remote sensing imagery data through a web platform. This contribution overcomes the current state of art, characterized by the separation of the two areas, which requires a continuous involvement of remote sensing experts in thematic mapping intensive tasks: labour intensive tasks are supplemented by the integration of the scalability capabilities from machine learning engines and web map servers. With this hypothesis the application field referred to the semi-automatic creation of large scale thematic maps can open up different fields, from agriculture to the environmental monitoring field, to expert users of these applications domains with limited specific knowledge of remote sensing techniques. Semantic tagging algorithms based on supervised classification methods can be exploited for thematic map creation from raster data based on user needs. This requires the integration of machine learning capabilities within web map servers, along with a simple interface that enables navigation and the monitoring of geospatial learning. The adaptive nature of this learning, along with its integration into a web server, requires a classification algorithm characterized by efficient management and processing of data in time scales compatible with traditional web browsing. At the same time, the volume of data managed by remote sensing applications motivates the transfer of the developed methodology to cloud environments under the Big Data paradigm. Ph.D. work developed by Dr. Javier Lozano in Vicomtech-IK4 and presented at the University of the Basque Country. Directed by: Dr. Ekaitz Zulueta and Dr. Marco Quartulli. More information: [email protected]
Views: 654 Vicomtech
Gavagai develops scalable and efficient algorithms for building large-scale semantic memories from streaming text data. This talk gives a brief overview of the technologies and touches upon notions such as Big Data, Text Analysis, Semantic Memories, and Deep Learning.
Views: 584 RISE SICS
Lecture starts at 3:00 The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open source machine learning algorithms. If you are a data scientist working with R, the caret package (short for [C]lassification [A]nd [RE]gression [T]raining) is a must-have tool in your toolbelt. The caret package provides capabilities that are ubiquitous in all stages of the data science project lifecycle. Most important of all, caret provides a common interface for training, tuning, and evaluating more than 200 machine learning algorithms. Not surprisingly, caret is a sure fire way to accelerate your velocity as a data scientist! In this presentation Dave Langer will provide an introduction to the caret package. The focus of the presentation will be using caret to implement some of the most common tasks of the data science project lifecycle and to illustrate incorporating caret into your daily work. Attendees will learn how to: • Create stratified random samples of data useful for training machine learning models. • Train machine learning models using caret’s common interface. • Leverage caret’s powerful features for cross-validation and hyperparameter tuning. • Scale caret via use of multi-core, parallel training. • Increase their knowledge of caret’s many features. R code and accompanying dataset: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Machine%20Learning%20with%20R%20and%20Caret caret website: http://topepo.github.io/caret/index.html Learn more about David here: https://www.meetup.com/data-science-dojo/events/239730653/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz9yY0 Watch the latest video tutorials here: https://hubs.ly/H0hz9fB0 See what our past attendees are saying here: https://hubs.ly/H0hz9Bn0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 45299 Data Science Dojo
Meet the authors of the e-book “From Words To Wisdom”, right here in this webinar on Tuesday May 15, 2018 at 6pm CEST. Displaying words on a scatter plot and analyzing how they relate is just one of the many analytics tasks you can cover with text processing and text mining in KNIME Analytics Platform. We’ve prepared a small taste of what text mining can do for you. Step by step, we’ll build a workflow for topic detection, including text reading, text cleaning, stemming, and visualization, till topic detection. We’ll also cover other useful things you can do with text mining in KNIME. For example, did you know that you can access PDF files or even EPUB Kindle files? Or remove stop words from a dictionary list? That you can stem words in a variety of languages? Or build a word cloud of your preferred politician’s talk? Did you know that you can use Latent Dirichlet Allocation for automatic topic detection? Join us to find out more! Material for this webinar has been extracted from the e-book “From Words to Wisdom” by Vincenzo Tursi and Rosaria Silipo: https://www.knime.com/knimepress/from-words-to-wisdom At the end of the webinar, the authors will be available for a Q&A session. Please submit your questions in advance to: [email protected] This webinar only requires basic knowledge of KNIME Analytics Platform which you can get in chapter one of the KNIME E-Learning Course: https://www.knime.com/knime-introductory-course
Views: 4724 KNIMETV
Finding large near-cliques in massive networks is a notoriously hard problem of great importance to many applications, including anomaly detection in security, community detection in social networks, and mining the Web graph. How can we exploit idiosyncrasies of real-world networks in order to solve this NP-hard problem efficiently? Can we find dense subgraphs in graph streams with a single pass over the stream? Can we design near real time algorithms for time-evolving networks? In this talk I will answer these questions in the affirmative. I will also present state-of-the-art exact and approximation algorithms for extraction of large near-cliques from large-scale networks, the k-clique densest subgraph problem, which run in a few seconds on a typical laptop. I will present graph mining applications, including anomaly detection in citation networks, and planning a successful cocktail party. I will conclude my talk with some interesting research directions.
Views: 245 MMDS Foundation
Frequent Itemsets Mining With Differential Privacy Over Large Scale Data IEEE PROJECTS 2018-2019 TITLE LIST Call Us: +91-7806844441,9994232214 Mail Us: [email protected] Website: : http://www.nextchennai.com : http://www.ieeeproject.net : http://www.projectsieee.com : http://www.ieee-projects-chennai.com : http://www.24chennai.com WhatsApp : +91-7806844441 Chat Online: https://goo.gl/p42cQt Support Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Video Tutorials * Supporting Softwares Support Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * Remote Connectivity * Document Customization * Live Chat Support
Views: 36 PONDYIT
Provides easy to apply example of eXtreme Gradient Boosting XGBoost Algorithm with R . Data: https://goo.gl/VoHhyh R file: https://goo.gl/qFPsmi Machine Learning videos: https://goo.gl/WHHqWP Includes, - Packages needed and data - Partition data - Creating matrix and One-Hot Encoding for Factor variables - Parameters - eXtreme Gradient Boosting Model - Training & test error plot - Feature importance plot - Prediction & confusion matrix for test data - Booster parameters R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 23757 Bharatendra Rai
How to work with images in Orange, what are image embeddings and how do perform clustering with embedded data. For more information on image clustering, read the blog: [Image Analytics: Clustering] https://blog.biolab.si/2017/04/03/image-analytics-clustering/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 20854 Orange Data Mining
USpan: an efficient algorithm for mining high utility sequential patterns KDD 2012 Junfu Yin Zhigang Zheng Longbing Cao Sequential pattern mining plays an important role in many applications, such as bioinformatics and consumer behavior analysis. However, the classic frequency-based framework often leads to many patterns being identified, most of which are not informative enough for business decision-making. In frequent pattern mining, a recent effort has been to incorporate utility into the pattern selection framework, so that high utility (frequent or infrequent) patterns are mined which address typical business concerns such as dollar value associated with each pattern. In this paper, we incorporate utility into sequential pattern mining, and a generic framework for high utility sequence mining is defined. An efficient algorithm, USpan, is presented to mine for high utility sequential patterns. In USpan, we introduce the lexicographic quantitative sequence tree to extract the complete set of high utility sequences and design concatenation mechanisms for calculating the utility of a node and its children with two effective pruning strategies. Substantial experiments on both synthetic and real datasets show that USpan efficiently identifies high utility sequences from large scale data with very low minimum utility.
Views: 101 Research in Science and Technology
The authors of this work propose an algorithm that determines optimal search keyword combinations for querying online product data sources in order to minimize identification errors during the product feature extraction process. Data-driven product design methodologies based on acquiring and mining online product-feature-related data are faced with two fundamental challenges: 1) determining optimal search keywords that result in relevant product related data being returned and 2) determining how many search keywords are sufficient to minimize identification errors during the product feature extraction process. These challenges exist because online data, which is primarily textual in nature, may violate several statistical assumptions relating to the independence and identical distribution of samples relating to a query. Existing design methodologies have predetermined search terms that are used to acquire textual data online, which makes the resulting data acquired, a function of the quality of the search term(s) themselves. Furthermore, the lack of independence and identical distribution of text data from online sources, impacts the quality of the acquired data. For example, a designer may search for a product feature using the term “screen”, which may return relevant results such as “the screen size is just perfect”, but may also contain irrelevant noise such as “researchers should really screen for this type of error”. A text mining algorithm is introduced to determine the optimal terms without labeled training data that would maximize the veracity of the data acquired to make a valid conclusion. A case study involving real-world smartphones is used to validate the proposed methodology.
Authors: Tianbao Yang, Qihang Lin, Rong Jin Abstract: As the scale and dimensionality of data continue to grow in many applications of data analytics (e.g., bioinformatics, finance, computer vision, medical informatics), it becomes critical to develop efficient and effective algorithms to solve numerous machine learning and data mining problems. This tutorial will focus on simple yet practically effective techniques and algorithms for big data analytics. In the first part, we plan to present the state-of-the-art large-scale optimization algorithms, including various stochastic gradient descent methods, stochastic coordinate descent methods and distributed optimization algorithms, for solving various machine learning problems. In the second part, we will focus on randomized approximation algorithms for learning from large-scale data. We will discuss i) randomized algorithms for low-rank matrix approximation; ii) approximation techniques for solving kernel learning problems; iii) randomized reduction methods for addressing the high-dimensional challenge. Along with the description of algorithms, we will also present some empirical results to facilitate understanding of different algorithms and comparison between them. ACM DL: http://dl.acm.org/citation.cfm?id=2789989 DOI: http://dx.doi.org/10.1145/2783258.2789989
Views: 464 Association for Computing Machinery (ACM)
Lecture notes: http://learning.stat.purdue.edu/mlss/_media/mlss/bottou.pdf Large-scale Machine Learning and Stochastic Algorithms During the last decade, data sizes have outgrown processor speed. We are now frequently facing statistical machine learning problems for which datasets are virtually infinite. Computing time is then the bottleneck. The first part of the lecture centers on the qualitative difference between small-scale and large-scale learning problem. Whereas small-scale learning problems are subject to the usual approximation--estimation tradeoff, large-scale learning problems are subject to a qualitatively different tradeoff involving the computational complexity of the underlying optimization algorithms in non-trivial ways. Unlikely optimization algorithm such as stochastic gradient show amazing performance for large-scale machine learning problems. The second part makes a detailed overview of stochastic learning algorithms applied to both linear and nonlinear models. In particular I would like to spend time on the use of stochastic gradient for structured learning problems and on the subtle connection between nonconvex stochastic gradient and active learning. See other lectures at Purdue MLSS Playlist: http://www.youtube.com/playlist?list=PL2A65507F7D725EFB&feature=view_all
Views: 470 Purdue University
Large scale sentiment learning with limited labels Vasileios Iosifidis (Leibniz University of Hanover) Eirini Ntoutsi (Leibniz University of Hanover) Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated in the social media on a daily basis. Although there is a lot of work on sentiment analysis, there are no many datasets available which one can use for developing new methods and for evaluation. To the best of our knowledge, the largest dataset for sentiment analysis is TSentiment, a 1.6 millions machine-annotated tweets dataset covering a period of about 3 months in 2009. This dataset however is too short and therefore insufficient to study heterogeneous, fast evolving streams. Therefore, we annotated the Twitter dataset of 2015 (275 million tweets) and we make it publicly available for research. For the annotation we leveraged the power of unlabeled data, together with labeled data which we derived using emoticons and emoticon-lexicons, using semi-supervised learning and in particular, Self-Learning and Co-Training. Our main contribution is the provision of the TSentiment15 dataset together with insights from the analysis, which includes both batch-and stream-processing of the data. In the former, all labeled and unlabeled data are available to the algorithms from the beginning, whereas in the later, they are revealed gradually based on their arrival time in the stream. More on http://www.kdd.org/kdd2017/
Views: 592 KDD2017 video
Part 1 of 2: Dr. Karianne Bergen, Harvard Data Science Initiative Fellow at Harvard U., presents "Big data for small earthquakes: a data mining approach to large-scale earthquake detection" at the MIT Earth Resources Laboratory on September 28, 2018. "Earthquake detection, the problem of extracting weak earthquake signals from continuous waveform data recorded by sensors in a seismic network, is a critical and challenging task in seismology. New algorithmic advances in “big data” and artificial intelligence have created opportunities to advance the state-of-the-art in earthquake detection algorithms. In this talk, I will present Fingerprint and Similarity Thresholding (FAST; Yoon et al, 2015), a data mining approach to large-scale earthquake detection, inspired by technology for rapid audio identification. FAST leverages locality sensitive hashing (LSH), a technique for efficiently identifying similar items in large data sets, to detect new candidate earthquakes without template waveforms ("training data"). I will present recent algorithmic extensions to FAST that enable detection over a seismic network and limit false detections due to local correlated noise (Bergen & Beroza, 2018). Using the foreshock sequence prior to the 2014 Mw 8.2 Iquique earthquake as a test case, we demonstrate that our approach is sensitive and maintains a low false detections rate, identifying five times as many events as the local seismicity catalog with a false discovery rate of less than 1%. We show that our new optimized FAST software is capable of discovering new events with unknown sources in 10 years of continuous data (Rong et al, 2018). I will end the talk with recommendations, based on our experience developing the FAST detector, for how the solid Earth geoscience community can leverage machine learning and data mining to enable data-driven discovery. "
Views: 162 MIT Earth Resources Laboratory
WANT TO EXPERIENCE A TALK LIKE THIS LIVE? Barcelona: https://www.datacouncil.ai/barcelona New York City: https://www.datacouncil.ai/new-york-city San Francisco: https://www.datacouncil.ai/san-francisco Singapore: https://www.datacouncil.ai/singapore Download Slides: https://www.datacouncil.ai/talks/a-multi-armed-bandit-framework-for-recommendations-at-netflix?utm_source=youtube&utm_medium=social&utm_campaign=%20-%20DEC-SF-18%20Slides%20Download ABOUT THE TALK: In this talk, we will present a general multi-armed bandit framework for recommending titles to our 117M+ members on the Netflix homepage. A key aspect of our framework is closed loop attribution to link how our members respond to a recommendation. Our framework performs frequent updates of policies using user feedback collected from a past time interval window. We will take deeper look at the system architecture. We will illustrate the use of that framework by focusing on two example policies – a greedy exploit policy which maximize the probability a user will play a title and an incrementality-based policy. The latter is a novel online learning approach that takes the causal effect of a recommendation into account. An incrementality-based policy recommends titles that brings about the maximum increase in a specific quantity of interest, such as engagement. This helps discount the effect of recommendations when a user would have played anyway. We describe offline experiments and online A/B test results for both of these example policies. ABOUT THE SPEAKERS: Jaya Kawale is a Senior Research Scientist at Netflix working on problems related to targeting and recommendations. She received her PhD in Computer Science from the University of Minnesota and has published research papers at several top-tier conferences. Her main areas of interest are large scale machine learning and data mining. Elliot is a software engineer at Netflix on the Personalization Infrastructure team. Currently, he builds big data systems for personalizing recommendations for Netflix subscribers, using a variety of technologies including Scala, Spark/Spark Streaming, Kafka, and Cassandra. He graduated from UC Berkeley (B.S.) and Stanford (M.S.) and has previously worked at eBay and Apple. FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai Facebook: https://www.facebook.com/datacouncilai
Views: 4518 Data Council
Tensors are higher order extensions of matrices that can incorporate multiple modalities and encode higher order relationships in data. This session will present recently developed tensor algorithms for topic modeling and deep learning with vastly improved performance over existing methods. Topic models enable automated categorization of large document corpora, without requiring labeled data for training. They go beyond simple clustering since they allow for documents to have multiple topics. Tensor methods provide a fast and a guaranteed method for training these models. They incorporate co-occurrence statistics of triplets of words in documents. We are releasing a fast and a robust implementation that vastly outperform existing solutions while providing significantly faster training times and better topic quality. Moreover, training and inference are decoupled in our algorithm, so the user can select the relevant part based on their requirements. We will present benchmarks across multiple datasets of different sizes and AWS instance types, and provide notebook examples.
Views: 2046 Amazon Web Services
This Big Data Video will help you understand how Amazon is using Big Data is ued in their recommendation syatems. You will understand the importance of Big Data using case study. Recommendation systems have impacted or even redefined our lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer recommendations of products we might be interested in. Regardless of the perspectives, business or consumer, Recommendation systems have been immensely beneficial. And big data is the driving force behind Recommendation systems. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=Amazon-BigData-S4RL6prqtGQ&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 32065 Simplilearn
Provides steps for applying Image classification & recognition with easy to follow example. R file: https://goo.gl/fCYm19 Data: https://goo.gl/To15db Machine Learning videos: https://goo.gl/WHHqWP Uses TensorFlow (by Google) as backend. Includes, - load keras and EBImage packages - read images - explore images and image data - resize and reshape images - one hot encoding - sequential model - compile model - fit model - evaluate model - prediction - confusion matrix Image Classification & Recognition with Keras is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 22146 Bharatendra Rai
This Bioinformatics lecture explains the details about the sequence alignment. The mechanism and protocols of sequence alignment is explained in this video lecture on Bioinformatics. For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences, such as those present in natural language or in financial data. Very short or very similar sequences can be aligned by hand. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Instead, human knowledge is applied in constructing algorithms to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that "forces" the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. A variety of computational algorithms have been applied to the sequence alignment problem. These include slow but formally correct methods like dynamic programming. These also include efficient, heuristic algorithms or probabilistic methods designed for large-scale database search, that do not guarantee to find best matches. Global alignments, which attempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. (This does not mean global alignments cannot end in gaps.) A general global alignment technique is the Needleman--Wunsch algorithm, which is based on dynamic programming. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. The Smith--Waterman algorithm is a general local alignment method also based on dynamic programming. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 178826 Shomu's Biology
Google Tech Talk May 5, 2010 ABSTRACT Presented by Justin Ma. We explore online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. We show that this application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the distribution of features that typify malicious URLs is changing continuously. Using a real-time system we developed for gathering URL features, combined with a real-time source of labeled URLs from a large Web mail provider, we demonstrate that recently-developed online algorithms can be as accurate as batch techniques, achieving daily classification accuracies up to 99% over a balanced data set. Slides: http://cseweb.ucsd.edu/~jtma/google_talk/jtma-google10.pdf Justin Ma is a PhD candidate at UC San Diego advised by Stefan Savage, Geoff Voelker and Lawrence Saul. His research interests are in systems and networking with an emphasis on network security, and his current focus is the application of machine learning to problems in security. He will be joining UC Berkeley as a postdoc after graduation. [Home page: http://www.cs.ucsd.edu/~jtma/ ]
Views: 10558 GoogleTechTalks
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Based on the concept of strong rules, Rakesh Agrawal, Tomasz and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by Point of Sale (POS) systems in supermarkets. Pada vidio ini dijelaskan konsep dasar mengenai algoritma data mining yaitu association rules, parameter ukur association rules (support, confidance, lift ratio) dan penerapannya. Penerapan association rules tidak hanya dilakukan di bidang ekonomi melainkan industri, bioinformatics dan lain-lain. Penjelasan pada vidio ini di ambil dari berbagai jurnal yang menerappkan metode association rules serta mudah di pahami. lift ratio, confidence, support, industrial engineering, komputer science, data science, machine learning, data mining, market basket analisys, association rules Simple example association rules basic concept. Association rules making your pattern very awesome
Views: 758 LSMART Channel