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: 312 manju nath
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: 4886 Ranji Raj
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: 2395 San Francisco Bay ACM
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: 85656 MIT OpenCourseWare
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 introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 39535 MIT OpenCourseWare
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: 2696 Ranji Raj
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: 27 PONDYIT
Graduate student Jing discusses her data mining term project which uses the Apriori algorithm (market basket analysis) to mine association rules from a set of database transactions.
Views: 14623 CSDepartment St. Joes
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.
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: 52389 John Paul
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: 522 Technology mart
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: 572 KDD2017 video
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Views: 27742 Artificial Intelligence - All in One
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: 1908 HasGeek TV
Frequent Itemsets Mining with Differential Privacy over Large-scale Data S/W: JAVA, JSP, MYSQL IEEE 2018-19
Views: 66 SivaKumar ChennaiSunday
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: 132 KDD2017 video
Presenter: Tunca Dogan KanSiL, Department of Health Informatics, Graduate School of Informatics, ODTU European Molecular Biology Laboratory, European Bioinformatics Institute * This version doesn't have the annotations made by the presenter. To watch the original version, you can register for free and watch it here: https://www.bigmarker.com/bioinfonet/TuncaDogan Abstract: Machine learning and data mining techniques are frequently employed to make sense of large-scale and noisy biological/biomedical data accumulated in public servers. A key subject in this endeavour is the prediction of the properties of proteins such as their functions and interactions. Recently, deep learning (DL) based methods have outperformed the conventional machine learning algorithms in the fields of computer vision, natural language processing and artificial intelligence; which brought attention to their application to the biological data. In this talk, I'm going to explain the DL-based probabilistic computational methods we have recently developed in our research center (KanSiL, Graduate School of Informatics, ODTU); first, to predict the functions of the uncharacterised proteins (i.e., DEEPred); and second, to identify novel interacting drug candidate molecules for all potential targets in the human proteome (i.e., DEEPscreen) to serve the purposes of drug discovery and repositioning, together with the aim of biomedical data integration. Apart from the benefits of employing novel DL approaches, I'll also mention the limitations of DL-based techniques when applied on the biological data, to explain why deep learning alone cannot solve every problem related to bioinformatics.
Views: 200 RSG-Turkey
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: 66 Research in Science and Technology
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: 9896 Bharatendra Rai
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: 762 Xoviabcs
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: 128 INFORMS
Authors: Qiaozhu Mei, Department of Electrical Engineering and Computer Science, University of Michigan Jian Tang, Montreal Institute for Learning Algorithms (MILA), University of Montreal Abstract: Large-scale networks such as social networks, citation networks, the World Wide Web, and traffic networks are ubiquitous in the real world. Networks can also be constructed from text, time series, behavior logs, and many other types of data. Mining network data attracts increasing attention in academia and industry, covers a variety of applications, and influences the methodology of mining many types of data. A prerequisite to network mining is to find an effective representation of networks, which largely determines the performance of downstream data mining tasks. Traditionally, networks are usually represented as adjacency matrices, which suffer from data sparsity and high-dimensionality. Recently, there is a fast-growing interest in learning continuous and low-dimensional representations of networks. This is a challenging problem for multiple reasons: (1) networks data (nodes and edges) are sparse, discrete, and globally interactive; (2) real-world networks are very large, usually containing millions of nodes and billions of edges; and (3) real-world networks are heterogeneous. Edges can be directed, undirected or weighted, and both nodes and edges may carry different semantics. In this tutorial, we will introduce the recent progress on learning continuous and low-dimensional representations of large-scale networks. This includes methods that learn the embeddings of nodes, methods that learn representations of larger graph structures (e.g., an entire network), and methods that layout very large networks on extremely low (2D or 3D) dimensional spaces. We will introduce methods for learning different types of node representations: representations that can be used as features for node classification, community detection, link prediction, and network visualization. We will introduce end-to-end methods that learn the representation of the entire graph structure through directly optimizing tasks such as information cascade prediction, chemical compound classification, and protein structure classification, using deep neural networks. We will highlight open source implementations of these techniques. Link to tutorial: https://sites.google.com/site/pkujiantang/home/kdd17-tutorial More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 392 KDD2017 video
In the continuing big data revolution, Apache Spark’s open-source cluster computing framework has overtaken Hadoop MapReduce as the big data processing engine of choice. Spark maintains MapReduce’s linear scalability and fault tolerance, but offers two key advantages: Spark is much faster – as much as 100x faster for certain applications; and Spark is much easier to program, due to its inclusion of APIs for Python, Java, Scala, SQL and R, plus its user-friendly core data abstraction, the distributed data frame. In addition, Spark goes far beyond traditional batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming data, machine learning, and graph processing. This tutorial offers you an accessible introduction to large-scale distributed machine learning and data mining, and to Spark and its potential to revolutionize academic and commercial data science practices. The tutorial includes discussions of algorithm design, presentation of illustrative algorithms, relevant case studies, and practical advice and experience in writing Spark programs and running Spark clusters. Part I familiarizes you with fundamental Spark concepts, including Spark Core, functional programming a la MapReduce, RDDs/data frames/datasets, the Spark Shell, Spark Streaming and online learning, Spark SQL, MLlib, and more. Part 2 gives you hands-on algorithmic design and development experience with Spark, including building algorithms from scratch such as decision tree learning, association rule mining (aPriori), graph processing algorithms such as PageRank and shortest path, gradient descent algorithms such as support vector machines and matrix factorization, distributed parameter estimation, and deep learning. Your homegrown implementations will shed light on the internals of Spark’s MLlib libraries and on typical challenges in parallelizing machine learning algorithms. You will see examples of industrial applications and deployments of Spark.
Views: 205 Ms Jessica PEH _
In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 525056 Thales Sehn Körting
Google Tech Talks August 16, 2007 ABSTRACT The Internet went well beyond a technology artefact, increasingly becoming a social interaction tool. These interactions are usually complex and hard to analyze automatically, demanding the research and development of novel data mining techniques that handle the individual characteristics of each application scenario. Notice that these data mining techniques, similarly to other machine learning techniques, are intensive in terms of both computation and I/O, motivating the development of new paradigms, programming environments, and parallel algorithms that support scalable and efficient applications. In this talk we present some results that justify not only the need for developing these new techniques, as well as their parallelization. Wagner Meira Jr. obtained his PhD from the University of Rochester in 1997 and is currently Associate Professor at the Computer Science Department at Universidade Federal de Minas Gerais, Brazil. His research focuses on scalability and efficiency of large scale parallel and distributed systems, from massively parallel to Internet-based platforms, and on data mining algorithms, their parallelization, and application to areas such as information retrieval, bioinformatics, and e-governance. Google engEDU Speaker: Wagner Meira Jr
Views: 387 GoogleTalksArchive
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: 819 KDD2017 video
Kexin Xie, a Data Science Engineers, and Dr. Wanderley Liu, a senior member of the Data Science Engineering, discuss how Salesforce Einstein is the artificial intelligence layer that delivers predictions and recommendations based on the customer’s unique business processes and data. Einstein Journey Insight is one of the key product offered by Salesforce DMP to help marketers and publishers leverage AI to analyze billions of touchpoints across consumer journeys and discover the optimal paths to conversion, including insights about which channels, messages, and events perform best. Learn more here: https://databricks.com/session/theory-meets-reality-large-scale-frequent-pattern-mining-with-apache-spark-in-the-real-world Article you might like: https://databricks.com/session/accelerating-deep-learning-training-with-bigdl-and-drizzle-on-apache-spark
Views: 103 Databricks
Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi 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: 105074 Bharatendra Rai
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: 235 MMDS Foundation
Efficient Algorithms for Mining Top-K High Utility Itemsets TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com High utility itemsets (HUIs) mining is an emerging topic in data mining, which refers to discovering all itemsets having a utility meeting a user-specified minimum utility threshold min_util. However, setting min_util appropriately is a difficult problem for users. Generally speaking, finding an appropriate minimum utility threshold by trial and error is a tedious process for users. If min_util is set too low, too many HUIs will be generated, which may cause the mining process to be very inefficient. On the other hand, if min_util is set too high, it is likely that no HUIs will be found. In this paper, we address the above issues by proposing a new framework for top-k high utility itemset mining, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU (mining Top-K Utility itemsets) and TKO (mining Top-K utility itemsets in One phase) are proposed for mining such itemsets without the need to set min_util. We provide a structural comparison of the two algorithms with discussions on their advantages and limitations. Empirical evaluations on both real and synthetic datasets show that the performance of the proposed algorithms is close to that of the optimal case of state-of-the-art utility mining algorithms.
Views: 1057 jpinfotechprojects
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: 694 LSMART Channel
Author: Alex Beutel, Google Research New York, Google, Inc. Abstract: Can we model how fraudsters work to distinguish them from normal users? Can we predict not just which movie a person will like, but also why? How can we find when a student will become confused or where patients in a hospital system are getting infected? How can we effectively model large attributed graphs of complex interactions? In this dissertation we understand user behavior through modeling graphs. Online, users interact not just with each other in social networks, but also with the world around them—supporting politicians, watching movies, buying clothing, searching for restaurants and finding doctors. These interactions often include insightful contextual information as attributes, such as the time of the interaction and ratings or reviews about the interaction. The breadth of interactions and contextual information being stored presents a new frontier for graph modeling. To improve our modeling of user behavior, we focus on three broad challenges: (1) modeling abnormal behavior, (2) modeling normal behavior and (3) scaling machine learning. To more effectively model and detect abnormal behavior, we model how fraudsters work, catching previously undetected fraud on Facebook, Twitter, and Tencent Weibo and improving classification accuracy by up to 68%. By designing flexible and interpretable models of normal behavior, we can predict why you will like a particular movie. Last, we scale modeling of large hypergraphs by designing machine learning systems that scale to hundreds of gigabytes of data, billions of parameters, and are 26 times faster than previous methods. This dissertation provides a foundation for making graph modeling useful for many other applications as well as offers new directions for designing more powerful and flexible models. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 226 KDD2017 video
Algorithm Design Meets Big Data Bahman Bahmani Strata Conference + Hadoop World New York, NY Oct. 28--30, 2013 Many big data applications require distributed computations over a cluster of commodity machines, e.g., under the MapReduce framework. This distributed computational model leads to algorithmic tradeoffs (e.g., between computation, memory usage, and network communication) that are different from those traditionally considered by algorithm designers. We will present these tradeoffs, explain the properties that a scalable big data algorithm must possess, and then provide pragmatic techniques, such as filtering, modulation, and distributed sketching, to effectively design such algorithms for different applications. We will demonstrate these techniques through concrete examples from machine learning (e.g., large scale clustering) to social network analysis (e.g., community detection) and text analytics (e.g., similarity search). We will show how utilizing these algorithmic techniques can enable big data applications that would otherwise be simply infeasible even using the most modern big data architectures. Slides: http://goo.gl/tjoPCQ More information: http://strataconf.com/stratany2013/public/schedule/detail/30934
Views: 601 bahmanbahmani
Google Tech Talks May, 21 2008 ABSTRACT Jure LESKOVEC - Research Scientist Emergence of the web and cyberspace gave rise to detailed traces of human social activity. This offers great opportunities to analyze and model behaviors of millions of people. For example, we examined ''planetary scale'' dynamics of a full Microsoft Instant Messenger network that contains 240 million people, with more than 255 billion exchanged messages per month (4.5TB of data), which makes it the largest social network analyzed to date. In this talk I will focus on two aspects of the dynamics of large real-world networks: (a) dynamics of information diffusion and cascading behavior in networks, and (b) dynamics of the structure of time evolving networks. First, I will consider network cascades that are created by the diffusion process where behavior cascades from node to node like an epidemic. We study two related scenarios: information diffusion among blogs, and a viral marketing setting of 16 million product recommendations among 4 million people. Motivated by our empirical observations we develop algorithms for detecting disease outbreaks and finding influential bloggers that create large cascades. We exploit the ''submodularity'' principle to develop an efficient algorithm that finds near optimal solutions, while scaling to large problems and being 700 times faster than a simple greedy solution. Second, in our recent work we found counter intuitive patterns that change some of the basic assumptions about fundamental structural properties of networks varying over time. Leveraging our observations we developed a Kronecker graph generator model that explains processes governing network evolution. Moreover, we can fit the model to large networks, and then use it to generate realistic graphs and give formal statements about their properties. Estimating the model naively takes O(N!N^2) while we develop a linear time O(E) algorithm. This talk will be taped. Speaker: Jure LESKOVEC - Research Scientist Jure Leskovec (www.cs.cmu.edu/~jure) is a PhD candidate in Machine Learning Department at Carnegie Mellon University. He is also a Microsoft Research Graduate Fellow. He received the ACM KDD 2005 and ACM KDD 2007 best paper awards, won the ACM KDD cup in 2003 and topped the Battle of the Sensor Networks 2007 competition. Jure holds three patents. His research interests include applied machine learning and large-scale data mining focusing on the analysis and modeling of large real-world networks as the study of phenomena across the social, technological, and natural worlds.
Views: 19170 GoogleTechTalks
Video shows what data mining means. A technique for searching large-scale databases for patterns; used mainly to find previously unknown correlations between variables that may be commercially useful. Data mining Meaning. How to pronounce, definition audio dictionary. How to say data mining. Powered by MaryTTS, Wiktionary
Views: 534 SDictionary
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: 20945 Bharatendra Rai
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: 107 MIT Earth Resources Laboratory
An Online Hierarchical Algorithm for Extreme Clustering Ari Kobren (University of Massachusetts Amherst) Nicholas Monath (University of Massachusetts Amherst) Akshay Krishnamurthy (University of Massachusetts Amherst) Andrew McCallum (University of Massachusetts Amherst) Many modern clustering methods scale well to a large number of data items, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for online hierarchical clustering that scales to both massive N and K-a problem setting we term extreme clustering. Our algorithm efficiently routes new data points to the leaves of an incrementally-built tree. Motivated by the desire for both accuracy and speed, our approach performs tree rotations for the sake of enhancing subtree purity and encouraging balancedness. We prove that, under a natural separability assumption, our non-greedy algorithm will produce trees with perfect dendrogram purity regardless of online data arrival order. Our experiments demonstrate that PERCH constructs more accurate trees than other tree-building clustering algorithms and scales well with both N and K, achieving a higher quality clustering than the strongest flat clustering competitor in nearly half the time. More on http://www.kdd.org/kdd2017/
Views: 1118 KDD2017 video
In this tutorial I cover the fastICA algorithm of Scikit-Learn, and how we can use it in the blind source separation of random generated data. Blind Signal Separation--, also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. This problem is in general highly underdetermined, but useful solutions can be derived under a surprising variety of conditions. Much of the early literature in this field focuses on the separation of temporal signals such as audio. However, blind signal separation is now routinely performed on multidimensional data, such as images and tensors, which may involve no time dimension whatsoever. The Shogun Machine learning toolbox provides a wide range of unified and efficient Machine Learning (ML) methods. The toolbox seamlessly allows to easily combine multiple data representations, algorithm classes, and general purpose tools. This enables both rapid prototyping of data pipelines and extensibility in terms of new algorithms. We combine modern software architecture in C++ with both efficient low-level computing backends and cutting edge algorithm implementations to solve large-scale Machine Learning problems (yet) on single machines. One of Shogun's most exciting features is that you can use the toolbox through a unified interface from C++, Python, Octave, R, Java, Lua, C#, etc. This not just means that we are independent of trends in computing languages, but it also lets you use Shogun as a vehicle to expose your algorithm to multiple communities. We use SWIG to enable bidirectional communication between C++ and target languages. Shogun runs under Linux/Unix, MacOS, Windows. Scikit-Learn Machine Learning in Python with simple and efficient tools for data mining and data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. It has an Open source, commercially usable - BSD license scikit-learn Machine Learning in Python Simple and efficient tools for data mining and data analysis Accessible to everybody, and reusable in various contexts Built on NumPy, SciPy, and matplotlib Open source, commercially usable - BSD license
Views: 11290 Francesco Piscani
On Friday, October 10, 2014, Michael Mahoney, a senior researcher at ICSI and an associate adjunct professor in UC Berkeley's Statistics Department, spoke about large-scale data analysis. This talk was part of ICSI's annual research review. Read the full abstracts for this and other talks given at the review at https://www.icsi.berkeley.edu/icsi/events/2014/10/research-review Abstract: Computer scientists have historically adopted quite different views on data (and thus on data management and data analysis) than statisticians, natural scientists, social scientists, and nearly everyone else who uses computation as a tool toward some downstream goal. For example, the former tend to view the data as noiseless bits and focus on algorithms with bounds on worst-case running time, independent of the input; while the latter typically have, either explicitly or implicitly, an underlying statistical model in mind and are interested in using computation and data to gain insight into the world. These issues are relevant now that “large-scale data analysis" has gone from being a technical topic of interest to a subset of computer scientists, to a cultural phenomenon that has a direct effect on nearly everyone. In this talk, I'll share some of my thoughts on these topics, I'll describe two applications (one in social network analysis and one in human genetics) where challenges related to these issues arose and describe how we dealt with them, and I'll offer some thoughts on how this so-called “Big Data" area might evolve.
Views: 347 ICSIatBerkeley
Abstract: Massive natural language datasets are now widely available for public use. Given the size of these datasets, even the simplest language models, such as n-gram analyses, require considerable computational power. The necessary computational requirements impose soft limits—available only to those trained in computational efficiency—to these rich datasets even though they are free to use. To help bridge computational efficiency with behavioral research agendas, my colleagues and I developed the R package, cmscu, a replacement to the standard DocumentTermMatrix function in R’s tm package. I will show how cmscu can be used to implement some of the most sophisticated n-gram algorithms. Instructor: David W. Vinson (University of California, Merced) --- Part of the Data on the Mind 2017 summer workshop: http://www.dataonthemind.org/2017-workshop Funded by the Estes Fund: http://www.psychonomic.org/page/estesfund Organized in collaboration with Data on the Mind: http://www.dataonthemind.org Videography by DeNoise Studios: http://www.denoise.com Workshop hashtag: #dataonthemind
Views: 429 Berkeley Institute for Data Science (BIDS)
Computing connected components of a graph lies at the core of many data mining algorithms, and is a fundamen-tal subroutine in graph clustering. This problem is well studied, yet many of the algorithms with good theoretical guarantees perform poorly in practice, especially when faced with graphs with billions of edges. We design improved al-gorithms based on traditional MapReduce architecture for large scale data analysis. We also explore the eﬀect of aug-menting MapReduce with a distributed hash table (DHT) service. These are the fastest algorithms that easily scale to graphs with hundreds of billions of edges.
Views: 1089 MMDS Foundation
Provides steps for applying artificial neural networks to do classification and prediction. R file: https://goo.gl/VDgcXX Data file: https://goo.gl/D2Asm7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - neural network model - input, hidden, and output layers - min-max normalization - prediction - confusion matrix - misclassification error - network repetitions - example with binary data neural network is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X. 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: 26510 Bharatendra Rai
evaluation of predictive data mining algorithms in soil data classification for optimized crop - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORKING 1. A Non-Monetary Mechanism for Optimal Rate Control Through Efficient Cost Allocation 2. A Probabilistic Framework for Structural Analysis and Community Detection in Directed Networks 3. A Ternary Unification Framework for Optimizing TCAM-Based Packet Classification Systems 4. Accurate Recovery of Internet Traffic Data Under Variable Rate Measurements 5. Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach 6. Achieving High Scalability Through Hybrid Switching in Software-Defined Networking 7. Adaptive Caching Networks With Optimality Guarantees 8. Analysis of Millimeter-Wave Multi-Hop Networks With Full-Duplex Buffered Relays 9. Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic 10. Approximation Algorithms for Sweep Coverage Problem With Multiple Mobile Sensors 11. Asynchronously Coordinated Multi-Timescale Beamforming Architecture for Multi-Cell Networks 12. Attack Vulnerability of Power Systems Under an Equal Load Redistribution Model 13. Congestion Avoidance and Load Balancing in Content Placement and Request Redirection for Mobile CDN 14. Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network Architecture 15. Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market 16. Distributed Packet Forwarding and Caching Based on Stochastic NetworkUtility Maximization 17. Dynamic, Fine-Grained Data Plane Monitoring With Monocle 18. Dynamically Updatable Ternary Segmented Aging Bloom Filter for OpenFlow-Compliant Low-Power Packet Processing 19. Efficient and Flexible Crowdsourcing of Specialized Tasks With Precedence Constraints 20. Efficient Embedding of Scale-Free Graphs in the Hyperbolic Plane 21. Encoding Short Ranges in TCAM Without Expansion: Efficient Algorithm and Applications 22. Enhancing Fault Tolerance and Resource Utilization in Unidirectional Quorum-Based Cycle Routing 23. Enhancing Localization Scalability and Accuracy via Opportunistic Sensing 24. Every Timestamp Counts: Accurate Tracking of Network Latencies Using Reconcilable Difference Aggregator 25. Fast Rerouting Against Multi-Link Failures Without Topology Constraint 26. FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks 27. Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services 28. Greenput: A Power-Saving Algorithm That Achieves Maximum Throughput in Wireless Networks 29. ICE Buckets: Improved Counter Estimation for Network Measurement 30. Incentivizing Wi-Fi Network Crowdsourcing: A Contract Theoretic Approach 31. Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks 32. Joint Resource Allocation for Software-Defined Networking, Caching, and Computing 33. Maximizing Broadcast Throughput Under Ultra-Low-Power Constraints 34. Memory-Efficient and Ultra-Fast Network Lookup and Forwarding Using Othello Hashing 35. Minimizing Controller Response Time Through Flow Redirecting in SDNs
Views: 5 Micans Infotech