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Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 31771 Red Apple Tutorials
Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 14516 Microsoft Research
A data mining approach for multivariate outlier detection in post processing
 
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A data mining approach for multivariate outlier detection in post processing -IEEE PROJECTS 2017-2018 HOME PAGE : http://www.micansinfotech.com/index.html CSE VIDEOS : http://www.micansinfotech.com/VIDEOS-2017-2018.html ANDROID VIDEOS : http://www.micansinfotech.com/VIDEOS-ANDROID-2017-2018.html PHP VIDEOS : http://www.micansinfotech.com/VIDEOS-APPLICATION-PROJECT-2017-2018#PHP APPLICATION VIDEOS : http://www.micansinfotech.com/VIDEOS-APPLICATION-PROJECT-2017-2018.html CSE IEEE TITLES : http://www.micansinfotech.com/IEEE-PROJECTS-CSE-2017-2018.html EEE TITLES : http://www.micansinfotech.com/IEEE-PROJECTS-POWERELECTRONICS-2017-2018.html MECHANICAL TITLES : http://www.micansinfotech.com/IEEE-PROJECTS-MECHANICAL-FABRICATION-2017-2018.html CONTACT US : http://www.micansinfotech.com/CONTACT-US.html MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM Output Videos… IEEE PROJECTS: https://www.youtube.com/channel/UCTgs... NS2 PROJECTS: https://www.youtube.com/channel/UCS-G... NS3 PROJECTS: https://www.youtube.com/channel/UCBzm... MATLAB PROJECTS: https://www.youtube.com/channel/UCK0Z... VLSI PROJECTS: https://www.youtube.com/channel/UCe0t... IEEE JAVA PROJECTS: https://www.youtube.com/channel/UCSCm... IEEE DOTNET PROJECTS: https://www.youtube.com/channel/UCSCm... APPLICATION PROJECTS: https://www.youtube.com/channel/UCVO9... PHP PROJECTS: https://www.youtube.com/channel/UCVO9... Micans Projects: https://www.youtube.com/user/MICANSIN...
Algorithms for Outlier Selection and One-Class Classification by Jeroen Janssens
 
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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 More info and slides here: http://www.hakkalabs.com/articles/outlier-selection-and-one-class-classification-by-jeroen-janssens In this talk, Jeroen Janssens, senior data scientist at YPlan, introduces both the outlier selection and one-class classification setting. He then presents a novel algorithm called Stochastic Outlier Selection (SOS). Below is the link to Jeroen's blogpost on the subject, it contains links to the d3 demo! http://jeroenjanssens.com/2013/11/24/stochastic-outlier-selection.html This talk is largely based on chapters 1, 2, and 4 of Jeroen's Ph.D. thesis (see https://github.com/jeroenjanssens/phd-thesis). In case you are just interested in the SOS algorithm itself, you can download the Technical Report, which corresponds to chapter 4 (see https://github.com/jeroenjanssens/sos). Jeroen will soon add a Python implementation of the SOS algorithm to the latter repository. outlier detection algorithm outlier detection algorithms algorithms for outlier detection FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai Facebook: https://www.facebook.com/datacouncilai
Views: 4689 Data Council
Data Cleaning Part-1 Basic Data Cleaning Operations
 
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This video discusses the Basics Operations of Data Cleaning. Datafile used in this video: https://goo.gl/aeDT2m PPT used in this video: https://goo.gl/JUBJgB
Views: 203 Neeraj Kaushik
Outliers Discovery from Smart Meters Data Using a  Statistical Based Data Mining Approach
 
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Outliers Discovery from Smart Meters Data Using a Statistical Based Data Mining Approach -IEEE PROJECTS 2017-2018 HOME PAGE : http://www.micansinfotech.com/index.html CSE VIDEOS : http://www.micansinfotech.com/VIDEOS-2017-2018.html ANDROID VIDEOS : http://www.micansinfotech.com/VIDEOS-ANDROID-2017-2018.html PHP VIDEOS : http://www.micansinfotech.com/VIDEOS-APPLICATION-PROJECT-2017-2018#PHP APPLICATION VIDEOS : http://www.micansinfotech.com/VIDEOS-APPLICATION-PROJECT-2017-2018.html CSE IEEE TITLES : http://www.micansinfotech.com/IEEE-PROJECTS-CSE-2017-2018.html EEE TITLES : http://www.micansinfotech.com/IEEE-PROJECTS-POWERELECTRONICS-2017-2018.html MECHANICAL TITLES : http://www.micansinfotech.com/IEEE-PROJECTS-MECHANICAL-FABRICATION-2017-2018.html CONTACT US : http://www.micansinfotech.com/CONTACT-US.html MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM Output Videos… IEEE PROJECTS: https://www.youtube.com/channel/UCTgs... NS2 PROJECTS: https://www.youtube.com/channel/UCS-G... NS3 PROJECTS: https://www.youtube.com/channel/UCBzm... MATLAB PROJECTS: https://www.youtube.com/channel/UCK0Z... VLSI PROJECTS: https://www.youtube.com/channel/UCe0t... IEEE JAVA PROJECTS: https://www.youtube.com/channel/UCSCm... IEEE DOTNET PROJECTS: https://www.youtube.com/channel/UCSCm... APPLICATION PROJECTS: https://www.youtube.com/channel/UCVO9... PHP PROJECTS: https://www.youtube.com/channel/UCVO9... Micans Projects: https://www.youtube.com/user/MICANSIN...
Data Mining with Weka (1.5: Using a filter )
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Using a filter http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 67830 WekaMOOC
Distributed Local Outlier Detection in Big Data
 
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Distributed Local Outlier Detection in Big Data Yizhou Yan (Worcester Polytechnic Institute) Lei Cao (Massachusetts Institute of Technology) Caitlin Kuhlman (Worcester Polytechnic Institute) Elke Rundensteiner (Worcester Polytechnic Institute) In this work, we present the first distributed solution for the Local Outlier Factor (LOF) method—a popular outlier detection technique shown to be very effective for datasets with skewed distributions. As datasets increase radically in size, highly scalable LOF algorithms leveraging modern distributed infrastructures are required. This poses significant challenges due to the complexity of the LOF definition, and a lack of access to the entire dataset at any individual compute machine. Our solution features a distributed LOF pipeline framework, called DLOF. Each stage of the LOF computation is conducted in a fully distributed fashion by leveraging our invariant observation for intermediate value management. Furthermore, we propose a data assignment strategy which ensures that each machine is self-sufficient in all stages of the LOF pipeline, while minimizing the number of data replicas. Based on the convergence property derived from analyzing this strategy in the context of real world datasets, we introduce a number of data-driven optimization strategies. These strategies not only minimize the computation costs within each stage, but also eliminate unnecessary communication costs by aggressively pushing the LOF computation into the early stages of the DLOF pipeline. Our comprehensive experimental study using both real and synthetic datasets confirms the efficiency and scalability of our approach to terabyte level data. More on http://www.kdd.org/kdd2017/
Views: 1939 KDD2017 video
Introduction to Data Mining: Data Transformation
 
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In this Data Mining Fundamentals tutorial, we discuss the transformation of data in data preprocessing, such as attribute transformation. Attribute transformation is a function that maps the entire set of values of a given attribute to a new set of replacement values such that each old value can be identified with one of the new values. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M860 See what our past attendees are saying here: https://hubs.ly/H0f8M870 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 7473 Data Science Dojo
Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Percentiles and Quartiles
 
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statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
Views: 414732 statslectures
Tutorial: Data Cleaning
 
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0:06 – Impossible Values and Response Sets 3:43 – Missing Data 7:45 – Outliers 11:33 – Normality
Views: 19474 Meredith Rocchi
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 74150 WekaMOOC
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 414110 Thales Sehn Körting
How to Use the Outliers Function in Excel
 
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See more: http://www.ehow.com/tech/
Views: 61075 eHowTech
Brian Kent: Density Based Clustering in Python
 
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PyData NYC 2015 Clustering data into similar groups is a fundamental task in data science. Probability density-based clustering has several advantages over popular parametric methods like K-Means, but practical usage of density-based methods has lagged for computational reasons. I will discuss recent algorithmic advances that are making density-based clustering practical for larger datasets. Clustering data into similar groups is a fundamental task in data science applications such as exploratory data analysis, market segmentation, and outlier detection. Density-based clustering methods are based on the intuition that clusters are regions where many data points lie near each other, surrounded by regions without much data. Density-based methods typically have several important advantages over popular model-based methods like K-Means: they do not require users to know the number of clusters in advance, they recover clusters with more flexible shapes, and they automatically detect outliers. On the other hand, density-based clustering tends to be more computationally expensive than parametric methods, so density-based methods have not seen the same level of adoption by data scientists. Recent computational advances are changing this picture. I will talk about two density-based methods and how new Python implementations are making them more useful for larger datasets. DBSCAN is by far the most popular density-based clustering method. A new implementation in Dato's GraphLab Create machine learning package dramatically speeds up DBSCAN computation by taking advantage of GraphLab Create's multi-threaded architecture and using an algorithm based on the connected components of a similarity graph. The density Level Set Tree is a method first proposed theoretically by Chaudhuri and Dasgupta in 2010 as a way to represent a probability density function hierarchically, enabling users to use all density levels simultaneous, rather than choosing a specific level as with DBSCAN. The Python package DeBaCl implements a modification of this method and a tool for interactively visualizing the cluster hierarchy. Slides available here: https://speakerdeck.com/papayawarrior/density-based-clustering-in-python Notebooks: http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_dbscan.ipynb http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_DeBaCl.ipynb
Views: 14896 PyData
Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong
 
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Real-time anomaly detection plays a key role in ensuring that the network operation is under control, by taking actions on detected anomalies. In this talk, we discuss a problem of the real-time anomaly detection on a non-stationary (i.e., seasonal) time-series data of several network KPIs. We present two anomaly detection algorithms leveraging machine learning techniques, both of which are able to adaptively learn the underlying seasonal patterns in the data. Jaeseong Jeong is a researcher at Ericsson Research, Machine Learning team. His research interests include large-scale machine learning, telecom data analytics, human behavior predictions, and algorithms for mobile networks. He received the B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST) in 2008, 2010, and 2014, respectively.
Views: 15177 RISE SICS
Final Year Projects | Distributed Strategies for Mining Outliers in Large Data Sets
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 80 myproject bazaar
Random Forest - Fun and Easy Machine Learning
 
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Random Forest - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ Hey Guys, and welcome to another Fun and Easy Machine Learning Algorithm on Random Forests. Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results. To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 205799 Augmented Startups
13. Classification
 
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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: 38888 MIT OpenCourseWare
Data Mining with Weka (3.2: Overfitting)
 
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Data Mining with Weka: online course from the University of Waikato Class X - Lesson X: Overfitting http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 27262 WekaMOOC
EXCEL PRO TIP: Outlier Detection
 
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For access to all pro tips, along with Excel project files, PDF slides, quizzes and 1-on-1 support, upgrade to the full course (75% OFF): https://courses.excelmaven.com/p/microsoft-excel-pro-tips FULL COURSE DESCRIPTION: This course is NOT an introduction to Excel. It's not about 101-style deep dives, or about showing off cheesy, impractical "hacks". It's about featuring some of Excel's most powerful and effective tools, and sharing them through crystal clear demos and unique, real-world case studies. We'll cover 75+ tools & techniques, organized into six categories: -Productivity -Formatting -Formulas -Visualization -PivotTables -Analytics Demos are self-contained and ranked by difficulty, so you can explore the content freely and master these tools and techniques in quick, bite-sized lessons. Full course includes LIFETIME access to: -10+ hours of high-quality video content -Downloadable PDF eBook -Excel project files (including data sets & solutions) -1-on-1 expert support -100% satisfaction guarantee (no questions asked!) Happy analyzing! -Chris (Founder, Excel Maven)
Views: 141 Excel Maven
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 283558 Last moment tuitions
Advanced Data Mining with Weka (3.6: Application: Functional MRI Neuroimaging data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Application: Functional MRI Neuroimaging data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1409 WekaMOOC
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 136600 Brandon Weinberg
NEW - Fraud and Anomaly Detection using Oracle Advanced Analytics Part 1 Concepts
 
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This is Part 1 of my Fraud and Anomaly Detection using Oracle Advanced Analytics presentations and demos series. Hope you enjoy! www.twitter.com/CharlieDataMine
Views: 6181 Charles Berger
Data mining final paper review
 
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This is the final presentation for the data mining final paper review please watch it Thank you
Views: 234 Akhil Kumar Mandoji
Data Mining with Weka (3.5: Pruning decision trees)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 5: Pruning decision trees http://weka.waikato.ac.nz/ Slides (PDF): https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 38220 WekaMOOC
The Basic Concept of Data Warehouse | What is DATA WAREHOUSING | Why #datawarehouse is important
 
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Data Warehousing a concept of storing Transformed data into a location where you can run your reports to make important business decisions. Many organizations use data warehouse to analyze sales, marketing etc. data to make important decisions. ETL is the tool that is used to transformed data from the initial load. If you have liked the video, please subscribe. Read our blogs - www.sqlultra.com Follow me on LinkedIn - https://www.linkedin.com/in/iqbalsqlexpert/
Views: 247 SQL ULTRA
Normal Distribution - Explained Simply (part 1)
 
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*** Check-out the improved version of this video here: https://youtu.be/tDLcBrLzBos I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1078378 how2stats
Data Mining with Weka (5.4: Summary)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 4: Summary http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 11515 WekaMOOC
Data cleaning in SPSS
 
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How to find and correct obvious errors using the software SPSS. More information is available on: http://science-network.tv/clean-data-file/
Views: 70847 Science Network TV
Data Mining & Business Intelligence | Tutorial #8 | Data Summarization Techniques
 
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Order my books at 👉 http://www.tek97.com/ #RanjiRaj #DataMining #DataSummarization This video addresses the data summarization techniques in data mining which are most frequently used! Watch now ! يتناول هذا الفيديو تقنيات تلخيص البيانات في مجال استخراج البيانات الأكثر استخدامًا! شاهد الآن ! Este video aborda las técnicas de resumen de datos en la minería de datos que se utilizan con mayor frecuencia. Ver ahora ! В этом видео чаще всего используются методы обобщения данных в области интеллектуального анализа данных! Смотри ! In diesem Video werden die am häufigsten verwendeten Datenzusammenfassungstechniken im Data Mining behandelt! Schau jetzt ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 1776 Ranji Raj
Final Year Projects | Information-Theoretic Outlier Detection for Large-Scale Categorical Data
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 48 myproject bazaar
Final Year Projects | Information-Theoretic Outlier Detection for Large-Scale Categorical Data
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 278 Clickmyproject
Advanced Data Mining with Weka (1.6: Application: Infrared data from soil samples)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Infrared data from soil samples http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2024 WekaMOOC
Orange 3
 
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In a series of videos, I am going to show you how to use data mining tool called Orange. This video is the first of a series and I will cover the basics. Please go to the following GitHub page and download the dataset before continuing on the series: https://github.com/RezaKatebi/Hands-on-experience-in-Orange-data-mining-toolkit
Views: 859 DataWiz
Support Vector Machine in R | SVM Algorithm Example | Data Science With R Tutorial | Simplilearn
 
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This Support Vector Machine in R tutorial video will help you understand what is Machine Learning, what is classification, what is Support Vector Machine (SVM), what is SVM kernel and you will also see a use case in which we will classify horses and mules from a given data set using SVM algorithm. SVM is a method of classification in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph. SVM is a classification algorithm used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Now, let us get started and understand Support Vector Machine in detail. Below topics are explained in this "Support Vector Machine in R" video: 1. What is machine learning? 2. What is classification? 3. What is support vector machine? 4. Understanding support vector machine 5. Understanding SVM kernel 6. Use case: classifying horses and mules To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/w72XBR Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Support-Vector-Machine-in-R-QkAmOb1AMrY&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - 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: 6774 Simplilearn
Build A Complete Project In Machine Learning | Credit Card Fraud Detection | Eduonix
 
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Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very interesting project to learn! It is one of the 10 projects from our course 'Projects in Machine Learning' which is currently running on Kickstarter. Eduonix Spring Sale | 18th - 24th March | Swing into spring with great learning. Now Courses at $10, Deals at $20 & E-Degrees at $29. Get it today before it’s gone! http://bit.ly/2UHMDph For this project, we will be using the several methods of Anomaly detection with Probability Densities. We will be implementing the two major algorithms namely, 1. A local out wire factor to calculate anomaly scores. 2. Isolation forced algorithm. To get started we will first build a dataset of over 280,000 credit card transactions to work on! You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML Want to learn Machine learning in detail? Then try our course Machine Learning For Absolute Beginners. Apply coupon code "YOUTUBE10" to get this course for $10 http://bit.ly/2Mi5IuP Kickstarter Campaign on AI and ML E-Degree is Launched. Back this Campaign and Explore all the Courses with over 58 Hours of Learning. Link- http://bit.ly/aimledegree Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: http://bit.ly/2nL2p59 ■ Linkedin: http://bit.ly/2nKWhKa ■ Instagram: http://bit.ly/2nL8TRu | @eduonix ■ Twitter: http://bit.ly/2eKnxq8
Box Plots
 
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Use this lesson and activity free at http://www.brainingcamp.com/resources/math/. Learn that Box and Whisker Plots are graphs that show the distribution of data along a number line. See how to construct box plots by ordering a data set to find the median of the set of data, median of the upper and lower quartiles, and upper and lower extremes. Draw a Box and Whisker plot and learn how to use box plots to solve a real world problem. See how to construct box plots if there are no middle values
Views: 730860 Brainingcamp
Why you, as entrepreneur, should read Outliers — The story of success?
 
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As an entrepreneur, all too often I see how success is just measured by money, but not that often I see people discussing how the successful got there in the first place. This book shows you another point of view what success is and how some people became very good at it! Mr Gladwell writes that success is a marriage between opportunity and time. He shows with real examples that it takes approximately 10.000 hours to master something. He shows you how cultural habits from hundreds of years ago still can play a role in your success. He comes with stories about Bill Gates and Korean Air, from a school in the Bronx up to his own Jamaican story. He explains why the best hockey players we know today are born on a January. Sounds crazy? I know many people are criticising this book, but only the fact the Malcolm Gladwell brings those stories from a different angle, feeds your creative thinking and as an entrepreneur we duty to challenge the status quo and only for that you should read it! I gave it 5 stars on Goodreads and I will read his other books as well! (https://www.goodreads.com/book/show/3228917-outliers) Buy and read to book https://www.amazon.co.uk/Outliers-Story-Success-Malcolm-Gladwell/dp/0141036257/ref=sr_1_1?linkCode=sl2&tag=gifvoukio-21 What are your thoughts on this book?
An Efficient Approach for Outlier Detection with Imperfect Data Labels
 
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An Efficient Approach for Outlier Detection with Imperfect Data Labels +91-9994232214,8144199666, [email protected], www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2014 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:[email protected] http://ieee.projectsieee.com/Cloud-Computing http://ieee.projectsieee.com/Data-Mining http://ieee.projectsieee.com/Android http://ieee.projectsieee.com/Image-Processing http://ieee.projectsieee.com/Networking http://ieee.projectsieee.com/Network-Security http://ieee.projectsieee.com/Mobile-Computing http://ieee.projectsieee.com/Parallel-Distributed http://ieee.projectsieee.com/Wireless-Communication http://ieee.projectsieee.com/NS2-Projects http://ieee.projectsieee.com/Matlab Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support
Views: 161 PROJECTS2014
Dealing with Class Imbalance using Thresholding
 
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Author: Rumi Ghosh, Robert Bosch LLC. Abstract: We propose thresholding as an approach to deal with class imbalance. We define the concept of thresholding as a process of determining a decision boundary in the presence of a tunable parameter. The threshold is the maximum value of this tunable parameter where the conditions of a certain decision are satisfied. We show that thresholding is applicable not only for linear classifiers but also for non-linear classifiers. We show that this is the implicit assumption for many approaches to deal with class imbalance in linear classifiers. We then extend this paradigm beyond linear classification and show how non-linear classification can be dealt with under this umbrella framework of thresholding. The proposed method can be used for outlier detection in many real-life scenarios like in manufacturing. In advanced manufacturing units, where the manufacturing process has matured over time, the number of instances (or parts) of the product that need to be rejected (based on a strict regime of quality tests) becomes relatively rare and are defined as outliers. How to detect these rare parts or outliers beforehand? How to detect combination of conditions leading to these outliers? These are the questions motivating our research. This paper focuses on prediction of outliers and conditions leading to outliers using classification. We address the problem of outlier detection using classification. The classes are good parts (those passing the quality tests) and bad parts (those failing the quality tests and can be considered as outliers). The rarity of outliers transforms this problem into a class-imbalanced classification problem. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 2176 KDD2016 video
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 68353 WekaMOOC
box plot analysis in data mining MS Excel
 
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Box plot www.cs.gsu.edu/~cscyqz/courses/dm/slides/ch02.ppt How to do Box-plot Analysis in MS Excel, How to perform Boxplot analysis in Excel with Details Graph Generation and applying Whisker Line on it. calculating Quirtile on given range of data. calculate minium & Maximum .
Views: 1167 Sweven Developers
Mahalanobis Distance - intuitive understanding through graphs and tables
 
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After going through this video- you will know What is Mahalanobis Distance? Where it is used in linear discriminant analysis? Issues with Euclidian distance An intuitive understanding of Mahalanobis distance formula How does it get rid of some of the issues of Euclidian distance?
Views: 24297 Gopal Malakar