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Anomaly Detection
 
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Anomaly detection video lesson describes techniques used in Exploratory Data Analysis for determining outliers. Visit our online service Assignment4Student http://assignment4student.com to find more lessons or submit your programming or math homework assignment. Free PDF presentation download: http://assignment4student.com/images/lessons/anomaly_detection.pdf
Views: 275 Assignment4Student
K mean clustering algorithm with solve example
 
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#kmean 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: 352245 Last moment tuitions
Concept Drift Detector in Data Stream Mining
 
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Jorge Casillas, Shuo Wang, Xin Yao, Concept Drift Detection in Histogram-Based Straightforward Data Stream Classification, 6th International Workshop on Data Science and Big Data Analytics, IEEE International Conference on Data Mining, November 17-20, 2018 - Singapore http://decsai.ugr.es/~casillas/downloads/papers/casillas-ci44-icdm18.pdf This presentation shows a novel algorithm to accurately detect changes in non-stationary data streams in a very efficiently way. If you want to know how the yacare caiman, the cheetah and the racer snake are related to this research, do not stop watching the video! More videos here: http://decsai.ugr.es/~casillas/videos.html
Views: 134 Jorge Casillas
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: 414804 Thales Sehn Körting
Time series anomaly detection in real time.
 
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This shows an example of real-time time series anomaly discovery with rule density curve built using sliding window-based SAX discretization and grammatical inference with Sequitur. Our paper describing the approach: http://csdl.ics.hawaii.edu/techreports/2014/14-05/14-05.pdf (SAX parameters used: window 400, PAA size 8, Alphabet size 6)
Views: 4830 seninp
Machine learning anomalies detection (SAE).
 
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Proof-of-Concept implementation of Anomalies Detection. Made with SAE-based algorithms and trained on PASCAL datasets to recognise cars, bicycles and buses. SAE: https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf PASCAL visual objects classes: http://host.robots.ox.ac.uk/pascal/VOC/
Views: 43 Hydrosphere io
Outlier Detection in Medical Claims using Knime
 
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This video describes the workflow of outlier detection in medical claims available on the Knime Workflow Public Server. Some of the pop-up windows are not shown in the video but are available in the Knime Challenge write-up.
Views: 542 Ashley Griffin
Machine Learning Demo-1
 
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Machine Learning Demo-1
Views: 821 Big Data Trunk
Spatial Data Mining I: Essentials of Cluster Analysis
 
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Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 26568 Esri Events
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
 
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In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4720 MapR Technologies
Advanced Data Mining with Weka (2.2: Weka’s MOA package)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Weka’s MOA package http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3013 WekaMOOC
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: 74225 WekaMOOC
Anomaly Detection Using Metrics and Exception Logs | Whiteboard Walkthrough
 
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In this Whiteboard Walkthrough, Ted Dunning, Chief Applications Architect at MapR, will talk about how you can use logs containing metrics and exceptions to detect anomalies in the behavior of a micro-service. For related material on this topic see: “A Better Way to Build A Fraud Detector” Whiteboard Walkthrough video by Ted Dunning https://www.mapr.com/blog/better-way-build-fraud-detector-streaming-data-and-microservices-architecture-whiteboard Free pdf of the book "Practical Machine Learning: A New Look at Anomaly Detection" https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection
Views: 4193 MapR Technologies
Implementation of DBSCAN algorithm and comparing with Kmeans algorithm
 
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This tutorial is about 'Implementation of DBSCAN algorithm and comparing with Kmeans algorithm'. A correction from video: Please replace the word 'Homogeneity' by 'Purity'. In this tutorial, I tried to explain some important concepts like: 1. How to determine 'eps' value for a given dataset. 2. How to calculate purity of a cluster. One thing I din't mention in the tutorial. The value of minPts depends on how many clusters you want to generate. Let's say if you want to generate big clusters and less number of clusters then set minPts value high. Too low value of minPts leads to generate more clusters from noise points so try to avoid setting minPts value too low. High or low value for minPts is relative and strongly depends on the size of the dataset. Find the 'optimal epsilon (Eps) value' paper here: http://iopscience.iop.org/article/10.1088/1755-1315/31/1/012012/pdf Find details about Normalization here: https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range
Views: 2386 ScoobyData Doo
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
SAS Visual Data Mining and Machine Learning
 
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http://www.sas.com/vdmml Boost analytical productivity and solve your most complex problems faster with a single, integrated in-memory environment that's both open and scalable. SAS VISUAL DATA MINING AND MACHINE LEARNING SAS Visual Data Mining and Machine Learning supports the end-to-end data mining and machine-learning process with a comprehensive, visual (and programming) interface that handles all tasks in the analytical life cycle. It suits a variety of users and there is no application switching. From data management to model development and deployment, everyone works in the same, integrated environment. http://www.sas.com/vdmml SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 5143 SAS Software
DBSCAN Algorithm : Density Based Spatial Clustering of Applications With Noise | Data Science-ExcelR
 
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ExcelR: In this video, we will learn about, DBSCAN is a well-known data clustering algorithm that is commonly used in data.T he DBSCAN algorithm basically requires 2 parameters. Things you will learn in this video 1)What is density based clustering algorithm (DBSCAN) 2)How to determine EPS? 3)What is the core point? 4)What is a border point? 5)What is noise point? To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here " https://goo.gl/JTkWXo" SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For K-Means Clustering Tutorial click here https://goo.gl/PYqXRJ For Introduction to Clustering click here Introduction to Clustering | Cluster Analysis #ExcelRSolutions #DBSCAN#Differenttypesofclusterings#EPS#corepoint#borderpoint#noisepoint#DataScienceCertification #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
k-Means Clustering Algorithm: Part 2
 
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Part 2 of a series on the k-Means Clustering Algorithm. Part 1: https://www.youtube.com/watch?v=KBlpBF8-Pu4 k-Means PDF: https://bit.ly/gskmeans GitHub: https://github.com/goutham1220 Gooth: https://youtube.com/gooth
Views: 15 GSDataScience
K-means and Anomalous Clustering - Prof. Boris Mirkin
 
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Yandex School of Data Analysis Conference Machine Learning: Prospects and Applications https://yandexdataschool.com/conference I consider first a rather simple intuitive criterion of individual cluster analysis, the product of the average within-cluster similarity and the number of elements in it to be maximized, and bring forth its mathematical properties relating the criterion with high-density subgraphs and spectral clustering approach. Then I present a simple approximation anomalous cluster model leading to the criterion and families of very effective ADDI crisp clustering methods (Mirkin, 1987) and FADDIS fuzzy clustering methods (Mirkin, Nascimento, 2012); the latter leading to mysteries in the popular Laplace similarity data normalization. Then I show that the celebrated square-error k-means clustering criterion can be equivalently reformulated as of finding a partition consisting of the anomalous clusters. I will finish with a problem in consensus clustering to show that it is equivalent to the anomalous similarity clustering and present experimental results of the superiority of this approach over competition.
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: 27300 WekaMOOC
23: Mahalanobis distance
 
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Multivariate distance with the Mahalanobis distance. Using eigenvectors and eigenvalues of a matrix to rescale variables.
Views: 55889 Matthew E. Clapham
How DTW (Dynamic Time Warping) algorithm works
 
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In this video we describe the DTW algorithm, which is used to measure the distance between two time series. It was originally proposed in 1978 by Sakoe and Chiba for speech recognition, and it has been used up to today for time series analysis. DTW is one of the most used measure of the similarity between two time series, and computes the optimal global alignment between two time series, exploiting temporal distortions between them. Source code of graphs available at https://github.com/tkorting/youtube/blob/master/how-dtw-works.m The presentation was created using as references the following scientific papers: 1. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustic Speech and Signal Processing, v26, pp. 43-49. 2. Souza, C.F.S., Pantoja, C.E.P, Souza, F.C.M. Verificação de assinaturas offline utilizando Dynamic Time Warping. Proceedings of IX Brazilian Congress on Neural Networks, v1, pp. 25-28. 2009. 3. Mueen, A., Keogh. E. Extracting Optimal Performance from Dynamic Time Warping. available at: http://www.cs.unm.edu/~mueen/DTW.pdf
Views: 36333 Thales Sehn Körting
How SVM (Support Vector Machine) algorithm works
 
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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: 523432 Thales Sehn Körting
Support Vector Machines - The Math of Intelligence (Week 1)
 
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Support Vector Machines are a very popular type of machine learning model used for classification when you have a small dataset. We'll go through when to use them, how they work, and build our own using numpy. This is part of Week 1 of The Math of Intelligence. This is a re-recorded version of a video I just released a day ago (the audio/video quality is better in this one) Code for this video: https://github.com/llSourcell/Classifying_Data_Using_a_Support_Vector_Machine Please Subscribe! And like. And comment. that's what keeps me going. Course Syllabus: https://github.com/llSourcell/The_Math_of_Intelligence Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ More Learning resources: https://www.analyticsvidhya.com/blog/2015/10/understaing-support-vector-machine-example-code/ http://www.robots.ox.ac.uk/~az/lectures/ml/lect2.pdf http://machinelearningmastery.com/support-vector-machines-for-machine-learning/ http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf http://www.statsoft.com/Textbook/Support-Vector-Machines https://www.youtube.com/watch?v=_PwhiWxHK8o And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 149813 Siraj Raval
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: 68432 WekaMOOC
Anomaly Detection with Robust Deep Autoencoders
 
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Author: Chong Zhou, Department of Computer Science, Worcester Polytechnic Institute Abstract: Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains. However, in many real-world problems, large outliers and pervasive noise are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders. Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Our model is inspired by Robust Principal Component Analysis, and we split the input data X into two parts, $X = L_{D} + S$, where $L_{D}$ can be effectively reconstructed by a deep autoencoder and $S$ contains the outliers and noise in the original data X. Since such splitting increases the robustness of standard deep autoencoders, we name our model a "Robust Deep Autoencoder (RDA)". Further, we present generalizations of our results to grouped sparsity norms which allow one to distinguish random anomalies from other types of structured corruptions, such as a collection of features being corrupted across many instances or a collection of instances having more corruptions than their fellows. Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 2622 KDD2017 video
Unsupervised Anomaly-based Malware Detection using Hardware Features
 
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Unsupervised Anomaly-based Malware Detection using Hardware Features; Adrian Tang; Simha Sethumadhavan; Salvatore Stolfo Recent works have shown promise in detecting malware programs based on their dynamic microarchitectural execution patterns. Compared to higher-level features like OS and application observables, these microarchitectural features are efficient to audit and harder for adversaries to control directly in evasion attacks. These data can be collected at low overheads using widely available hardware performance counters (HPC) in modern processors. In this work, we advance the use of hardware supported lower-level features to detecting malware exploitation in an anomaly-based detector. This allows us to detect a wider range of malware, even zero days. As we show empirically, the microarchitectural characteristics of benign programs are noisy, and the deviations exhibited by malware exploits are minute. We demonstrate that with careful selection and extraction of the features combined with unsupervised machine learning, we can build baseline models of benign program execution and use these profiles to detect deviations that occur as a result of malware exploitation. We show that detection of real-world exploitation of popular programs such as IE and Adobe PDF Reader on a Windows/x86 platform works well in practice. We also examine the limits and challenges in implementing this approach in face of a sophisticated adversary attempting to evade anomaly-based detection. The proposed detector is complementary to previously proposed signature-based detectors and can be used together to improve security.
K-Means Clustering - Methods using Scikit-learn in Python - Tutorial 23 in Jupyter Notebook
 
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In this tutorial on Python for Data Science, you will learn about how to do K-means clustering/Methods using pandas, scipy, numpy and Scikit-learn libraries in Jupyter notebook. This is the 23th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets Download Link for Cars Data Set: https://www.4shared.com/s/fWRwKoPDaei Download Link for Enrollment Forecast: https://www.4shared.com/s/fz7QqHUivca Download Link for Iris Data Set: https://www.4shared.com/s/f2LIihSMUei https://www.4shared.com/s/fpnGCDSl0ei Download Link for Snow Inventory: https://www.4shared.com/s/fjUlUogqqei Download Link for Super Store Sales: https://www.4shared.com/s/f58VakVuFca Download Link for States: https://www.4shared.com/s/fvepo3gOAei Download Link for Spam-base Data Base: https://www.4shared.com/s/fq6ImfShUca Download Link for Parsed Data: https://www.4shared.com/s/fFVxFjzm_ca Download Link for HTML File: https://www.4shared.com/s/ftPVgKp2Lca
Views: 23629 TheEngineeringWorld
Fraud detection using machine learning & deep learning (Rubén Martínez) CyberCamp 2016 (English)
 
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Conference: Fraud detection using machine learning & deep learning The goal of this presentation is to go over several Machine Learning and Deep Learning techniques so as to detect fraud. Some of the algorithms and technologies that we intend to explain are, for instance, graphs, Neo4J, Apache Spark or Deep Learning libraries such as H2O. Rubén Martínez Sánchez: Computer Engineer by UPM and Master in Data Science. I have developed courses such as the title of Project Development with UML and Java also taught by UPM, CEH, Intel vPro, Cloudera Developer Training for Apache Hadoop, Cloudera Developer Training for Apache Spark, Introduction to Big Data with Apache Spark (Databricks) or Principles of Functional Programming in Scala among others. I have worked as a security auditor in StackOverflow as well as professor of the Postgraduate in Computer Security and Systems Hacking in the Polytechnic University School of Mataró or the online superior title of Computer Security and Hacking Systems Ethics of the Rey Juan Carlos University. I have also participated as a co-author of Ra-Ma publishing houses such as Hacking and Web Page Security (MundoHacker), Hacking and Internet Security Ed. 2011, etc. I am currently working on intelligent chatbots using Deep Learning. CyberCamp is the major cybersecurity event that INCIBE organises on a yearly basis for the purpose of identifying, appealing, managing and contributing to the creation of talent in cybersecurity that can be transferred to the private sector according to its demands. This initiative is one of the tasks that the Trust in the Digital Sphere Plan, included in the Spain’s Digital Agenda, has requested INCIBE to carry out. LEÓN - 2016 DECEMBER 1st, 2nd, 3rd and 4th.
Views: 4960 INCIBE
Column Configuration
 
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How to use the column configuration feature within Lucidity to control the columns displayed on the screen, and within reports.
Views: 179 Lucidity Software
Different Data Mining Approaches for Forecasting Use of Bike Sharing System
 
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R Codes are available on below link: https://github.com/mayurkmane/ADM-Project-A12-Group Document related to this data mining study is available on below link: https://www.dropbox.com/s/r5qw4mofej23gbg/Group-A12%20ADM%20Project.pdf?dl=0 https://ie.linkedin.com/in/mayurkmane
Views: 110 Mayur Mane
Computer Vision (SCICV) –  Automatic Converting Image to Data
 
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This tutorial will present a simple method using color of the line to convert it to data points automatically, and a final GUI would be presented to combine these 2 methods together. http://scilab.io/category/tutorial/
Views: 154 Chin Luh Tan
BigML - Streaming Histogram (wine alcohol content vs quality)
 
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This video shows a streaming histogram in action. The histogram uses a fixed amount of memory (16 bins in this example) and dynamically allocates bin positions and widths during one pass over the data. The histogram is extended so that it can capture information about a second feature. This lets us see potential correlations between variables. The plot shows the distribution of wine alcohol content for a variety of white wines (the red line) versus the average wine quality rating (the blue line). Each frame is rendered after the histogram is updated with data about 5 more varieties. In the last frame, the histogram has observed 2500 kinds of wine. The data is also streaming into the histogram in sorted order. This can be tricky for some streaming histogram techniques, but this algorithm handles it successfully. Thanks to Y. Ben-Haim and E. Tom-Tov for publishing the algorithm: http://jmlr.csail.mit.edu/papers/v11/ben-haim10a.html Thanks to S. Tyree, et. al. for the histogram extension: http://research.engineering.wustl.edu/~tyrees/Publications_files/fr819-tyreeA.pdf Thanks to the UCI Machine Learning Repository for the data: http://archive.ics.uci.edu/ml/datasets/Wine+Quality
Views: 463 bigmlcom
Tutorial Rapidminer Studio Bahasa Indonesia Bagian 1
 
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Pengenalan Interface Rapidminer Studio 7 3 yang meliputi pengenalan user interface pada Rapidminer Studio, meretrieve dataset publik, penjelasan beberapa dataset public, dan membuat Repository yang baru.
Views: 3384 Junta Zen
Knime Challenge GeoVisualization
 
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UNC SILS INLS 625 KNIME Challenge
Views: 44 Zihao Li
Unsupervised Extraction of Human-Interpretable Nonverbal Behavior
 
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http://www.cs.rochester.edu/u/mehoque/Publications/2015/2015-acmmm-tanveer-liu-hoque.pdf
Views: 39 Ehsan Hoque
Expected Distance between two Standard Normals Part 1
 
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We calculate the expected distance between two standard normal distributions.
Views: 209 Ben1994
Data Mining with Weka - Neural Networks and Random Forests
 
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Simple introduction video on how to run neural networks and random forests in weka.
Views: 12330 Gaurav Jetley
Vector Express Lesson 4 – Deploy a KNIME ETL workflow
 
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Once designed, ETL workflows are often executed in an automated fashion. This lesson teaches you how to use the KNIME headless batch mode to deploy an Actian Vector Express workflow. Actian Vector Express can be downloaded from here: http://bigdata.actian.com/express If you have any questions, try the Actian Vector Express FAQ here: http://img.en25.com/Web/Actian/%7B0dc75c40-c77f-4e77-a7f9-6550f3dd394f%7D_Vector_Express_FAQs_03132015.pdf If you have comments, get stuck, or just want to chat about your project, join the Actian Vector Community forum here: http://supportservices.actian.com/community
Views: 503 Actian Corporation
آموزش داده کاوی به زبان فارسیData Mining with RapidMiner (part5-Classification Text mining)
 
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در این ویدئو آموزشی داده کاوی را به صورت کاربردی و عملی با یک مثال کاربردی از دسته بندی متون(متن کاوی) در برنامه رپیدماینر فرابگیرید تهیه شده توسط محمدحسن فخاران در مجموعه عصر فناوری دانش(فرابر)
Views: 393 mh fakharan
Improved churn prediction telecommunication industry DMTs| Final Year Projects 2016 - 2017
 
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متن‌بازسازی کلان داده
 
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ارائه شده در جشنواره روز آزادی نرم‌افزار ۱۳۹۴ در تهران سایت جشنواره: http://sfd.fsug.ir/1394/ لینک اسلایدها: http://sfd.fsug.ir/1394/images/presentations/Hadi_Sotudeh_Open_Sourcing_Big_Data.pdf لینک مقاله: http://sfd.fsug.ir/1394/component/rsform/?task=submissions.view.file&hash=72eb7363759b61d058ed8273d992e7f1 ارائه‌دهنده: هادی ستوده عبارت کلان‌داده به مجموعه‌های داده‌ای اشاره دارد که به اندازه‌ای بزرگ و حجیم هستند که با ابزارهای مدیریتی و پایگاه‌های داده سنتی و معمولی قابل مدیریت نیستند. مشکلات اصلی در کار با این نوع داده‌ها مربوط به برداشت و جمع‌آوری، ذخیره‌سازی، جست‌وجو، اشتراک‌گذاری، تحلیل و نمایش آن‌ها می‌باشد. کلان داده به عنوان یکی از فناوری‌های کلیدی و نوظهور به اذعان بسیاری از خبرگان می‌تواند تأثیرات شگرفی بر جای بگذارد. امروزه با گسترش شبکه‌های اجتماعی و ظهور منابع جدید اطلاعاتی، حجم داده‌های تولیدی به شکل روزافزونی در حال افزایش است. نظرات کاربران شبکه‌های اجتماعی، محتواهای به اشتراک گذاشته شده و اطلاعات ضبط شده توسط حسگرهای مختلف همگی از انواع منابعی هستند که در این انفجار اطلاعاتی نقش ایفا می‌کنند. با استفاده از تحلیل حجم‌های بیشتری از داده‌ها، می‌توان تحلیل‌های بهتر و پیشرفته‌تری را برای مقاصد مختلف، از جمله مقاصد تجاری، پزشکی و امنیتی، انجام داد و نتایج مناسب‌تری را دریافت‌کرد. پیوند موجود بین کلان داده و ابزارهای متن باز به وضوح با استفاده از ابزار هدوپ شروع شد و این روند در ادامه سرعت بیشتری به خود گرفت. برگزار شده توسط بنیاد دانش آزاد ایران مکان برگزاری جشنواره: دانشگاه صنعتی شریف - سالن جابرابن‌حیان
Principal component analysis
 
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Currell: Scientific Data Analysis. Minitab analysis for Figs 9.6 and 9.7 http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press
How K-Means algorithm works
 
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In this video I describe how the K-Means algorithm works, and provide a simple example using 2-dimensional data and K=3.
Views: 150301 Thales Sehn Körting
SAS Enterprise Miner: Impute, Transform, Regression & Neural Models
 
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http://support.sas.com/software/products/miner/index.html Chip Robie of SAS presents the fourth in a series of six "Getting Started with SAS Enterprise Miner 13.2" videos. This fourth video demonstrates imputing and transforming data, building a neural network, and building a regression model with SAS Enterprise Miner. For more information regarding SAS Enterprise Miner, please visit http://support.sas.com/software/products/miner/index.html SAS ENTERPRISE MINER SAS Enterprise Miner streamlines the data mining process so you can create accurate predictive and descriptive analytical models using vast amounts of data. Our customers use this software to detect fraud, minimize risk, anticipate resource demands, reduce asset downtime, increase response rates for marketing campaigns and curb customer attrition. LEARN MORE ABOUT SAS ENTERPRISE MINER http://www.sas.com/en_us/software/analytics/enterprise-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 42701 SAS Software
How to Make Data Amazing - Intro to Deep Learning #5
 
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In this video, we'll go through data preprocessing steps for 3 different datasets. We'll also go in depth on a dimensionality reduction technique called Principal Component Analysis. Coding challenge for this video: https://github.com/llSourcell/How_to_Make_Data_Amazing Charles-David's Winning Code: https://github.com/alkaya/earthquake-cotw Siby Jack Grove's Runner-up code: https://github.com/sibyjackgrove/Earthquake_predict/blob/master/earthquake_predict.ipynb Please subscribe. And like. And comment. That's what keeps me going. More Learning Resources: http://www.cs.ccsu.edu/~markov/ccsu_courses/datamining-3.html http://www.slideshare.net/jasonrodrigues/data-preprocessing-5609305 http://iasri.res.in/ebook/win_school_aa/notes/Data_Preprocessing.pdf http://staffwww.itn.liu.se/~aidvi/courses/06/dm/lectures/lec2.pdf http://ufldl.stanford.edu/wiki/index.php/Data_Preprocessing http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://plot.ly/ipython-notebooks/principal-component-analysis/ Public datasets: https://github.com/caesar0301/awesome-public-datasets https://aws.amazon.com/public-datasets/ http://archive.ics.uci.edu/ml/index.html https://dreamtolearn.com/ryan/1001_datasets Join us in our Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 49332 Siraj Raval