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Weka Tutorial 02: Data Preprocessing 101 (Data Preprocessing)
 
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This tutorial demonstrates various preprocessing options in Weka. However, details about data preprocessing will be covered in the upcoming tutorials.
Views: 162641 Rushdi Shams
Data Cleaning Process Steps / Phases [Data Mining] Easiest Explanation Ever (Hindi)
 
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Views: 8843 5 Minutes Engineering
A SURVEY ON WEB USAGE MINING TECHNIQUES
 
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International conference on E-commerce and Information Technology 2013 22-23 July 1013, Grand Oriental Hotel Colombo, Sri Lanka by - Mr. Abdul Rahaman wahab sait Lecturer, Shaqra University, Kingdom of Saudi Arabia (Research Scholar, Alagappa University,India)
Views: 949 ICRD Sri Lanka
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 50629 Well Academy
Lecture 12: TextMining
 
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Techniques to extract information from textual data. Course homepage: http://www.knoesis.org/cs4800-6800-spring2016
Views: 1152 Knoesis Center
Web Usage Mining  A Survey on Pattern Extraction from Web Logs
 
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Views: 769 Chennai Sunday
Data Collection and Preprocessing | Lecture 6
 
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Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 1152 Leo Isikdogan
Data Mining Classification and Prediction ( in Hindi)
 
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A tutorial about classification and prediction in Data Mining .
Views: 28255 Red Apple Tutorials
DATA MINING LOG FILES
 
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Views: 339 Hari Sainath
Introduction to WebMining - Part 1
 
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Introduction to Web Mining and its usage in E-Commerce Websites. This is part 1. This will contain introduction of the field and in part two we will discuss its usage in E-Commerce website. Please don't forget to give your feedback... :)
Views: 4730 zdev log
Genetic Algorithms Tutorial 07 - data mining + arithmetic operators + JAVA 8
 
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Website + download source code @ http://www.zaneacademy.com
Views: 1004 zaneacademy
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 442210 Brandon Weinberg
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 30465 GRIETCSEPROJECTS
Weka Tutorial 06: Discretization (Data Preprocessing)
 
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An important feature of Weka is Discretization where you group your feature values into a defined set of interval values. Experiments showed that algorithms like Naive Bayes works well with discretized feature values
Views: 57545 Rushdi Shams
Mining Temporal Patterns in Time Interval- Based Data | Final Year Projects 2016
 
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Views: 345 Clickmyproject
Data mining
 
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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 pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The 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. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1661 Audiopedia
System Event Mining: Algorithms and Applications part 2
 
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Authors: Genady Ya. Grabarnik, St. John's University Larisa Shwartz, IBM Thomas J. Watson Research Center Tao Li, Florida International University Abstract: Many systems, from computing systems, physical systems, business systems, to social systems, are only observable indirectly from the events they emit. Events can be defined as real-world occurrences and they typically involve changes of system states. Events are naturally temporal and are often stored as logs, e.g., business transaction logs, stock trading logs, sensor logs, computer system logs, HTTP requests, database queries, network traffic data, etc. These events capture system states and activities over time. For effective system management, a system needs to automatically monitor, characterize, and understand its behavior and dynamics, mine events to uncover useful patterns, and acquire the needed knowledge from historical log/event data. Event mining is a series of techniques for automatically and efficiently extracting valuable knowledge from historical event/log data and plays an important role in system management. The purpose of this tutorial is to present a variety of event mining approaches and applications with a focus on computing system management. It is mainly intended for researchers, practitioners, and graduate students who are interested in learning about the state of the art in event mining. Link to tutorial: https://users.cs.fiu.edu/~taoli/event-mining/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 46 KDD2017 video
VIDEO TUTORIAL PREPROCESSING DATA USING TOOLS DATA MINING WEKA 3 6
 
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DATA MINING COMPUTATIONAL SCIENCE TELKOM UNIVERSITY KELOMPOK 12 audio : Cash Cash - Surrender
Views: 1582 Larita Ditakristy
Time Series Data Mining Forecasting with Weka
 
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I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 23350 Web Educator
Text Mining Tutorials for Beginners | Importance of Text Mining | Data Science Certification -ExcelR
 
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ExcelR: Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Things you will learn in this video 1) What is Text mining? 2) How clustering techniques helps and text data analysis? 3) What is word cloud? 4) Examples for text mining 5) Text mining terminology and pre-processing 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 #Textmining #Whatistextmining #Textminingimportance #Wordcloud #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
Text Mining
 
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Views: 2403 Soumya shetty
How to use WEKA software for data mining tasks
 
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In this video, I'll guide you how to use WEKA software for preprocessing, classifying, clustering, association. WEKA is a collection of machine learning algorithms for performing data mining tasks. #RanjiRaj #WEKA #DataMining Follow me on Instagram πŸ‘‰ https://www.instagram.com/reng_army/ Visit my Profile πŸ‘‰ https://www.linkedin.com/in/reng99/ Support my work on Patreon πŸ‘‰ https://www.patreon.com/ranjiraj Get WEKA from here : http://www.cs.waikato.ac.nz/ml/weka/
Views: 16573 Ranji Raj
Data Mining :Preproccesing with Weka Tools
 
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Data Mining IF SIDE 01 2015 Teknik Informatika Telkom University Kelompok 8 Muhammad Jendro Yuwono 1103110085 Ahmad Ridwan Rezani 1103110102 Dwi Marlina Sari 1103110103
SD IEEE Dotnet 03 Criminals and crime hotspot detection using data mining algorithms
 
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Logistic Regression Node: Algorithm Settings
 
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KNIME Analytics Platform offers a number of Machine Learning algorithms. One of those is the Logistic Regression. The Logistic Regression Learner node is responsible for the training of a logistic regression model. Here is a quick description of the basic settings available in its configuration window. The workflow shown in this video can be found on the EXAMPLES server under 04_Analytics/04_Classification_and_Predictive_Modelling /06_Logistic_Regression. Other related videos: - Logistic Regression Node: Output Values and Memory Handling https://youtu.be/ywPvpgFF2i4
Views: 4675 KNIMETV
Advanced Data Mining with Weka (5.1: Invoking Python from Weka)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 1: Invoking Python from Weka http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/7XXl63 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4260 WekaMOOC
Introduction Spatial Data
 
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Excel Maps, applied to MS Power BI Course curriculum - http://bitly.com/Agenda-BI Cloud Hound website http://www.cloudhound.co.uk
Views: 2497 Cloud Hound
Introduction to Modeling Web Data
 
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Know about the web Data, Data Modeling and why it is required
Advanced ETL Functionalities and Machine Learning Pre-processing
 
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This video makes a list of some of the most commonly used advanced ETL functionalities for: - outlier detection, - dimensionality reduction, - feature generation, - feature selection, - imputing missing values, - automatic and manual, - simple and Machine Learning based, - involving coordinate transformations. Workflow is available on the KNIME EXAMPLES Server under 50_Applications/28_Predicting_Departure_Delays/01_Analytics This same workflow can be reproduced to run on a Spark and/or Hadoop platform still from within KNIME, as described in video "Scaling Analytics with Big data" https://youtu.be/b_ijiZdQB7g
Views: 3122 KNIMETV
Discovery of Ranking Fraud for Mobile Apps
 
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Title: Discovery of Ranking Fraud for Mobile Apps Domain: Data Mining Key Features: 1. Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. 2. We provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of global anomaly of App rankings. 3. We investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps’ ranking, rating and review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: β€’ 1 Review PPT β€’ 2nd Review PPT β€’ Full Coding with described algorithm β€’ Video File β€’ Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2016 - 2017 48. 2016 - 2017 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students 62. 2017 mini projects on data mining 63. latest mini projects on data mining 64. data mining projects for engineering students 65. cse projects on data mining 66. data mining related ieee projects 67. ieee projects in content mining 68. data mining ieee major projects 69. 2017 ieee projects on data mining with abstract 70. 2017 data mining with abstract 71. data mining projects with source code 72. data mining projects for students with demo 73. data mining projects with source code in java 74. data mining mini projects source code 75. list of mini projects in data mining 76. 2017 data mining mini projects topics 77. 2017 data mining related projects 78. 2017 real time data mining projects 79. 2017 data mining projects titles from IEEE
Algorithm for removal of irrelevant and elimination of redundant features in high dimensional data
 
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Spatial data mining Project
 
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A novel spatial data mining project on dengue done by DR M N RAO & P.VEDAVATHI From scet engineering college, Narsapuram.
Views: 696 M N RAO
Data Mining, Classification, Clustering, Association Rules, Regression, Deviation
 
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Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 85790 StudyYaar.com
Getting Started with Orange 03: Widgets and Channels
 
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Orange data mining widgets and communication channels. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 51919 Orange Data Mining
Text Mining: NGram Word Frequency in R
 
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Using R, you can see what how often words occur in an aggregated data set. It is often used in business for text mining of notes in tickets as well as customer surveys. Using a Corpus and TermDocumentMatrix in R we can organize the data accordingly to extract the most common word combos. Direct File: https://github.com/ProfessorPitch/ProfessorPitch/blob/master/R/NGram%20Wordcloud.R Software Versions: R 3.3.3 Java = jre1.8.0_171 (64 bit) R Packages: library(NLP) library(tm) library(RColorBrewer) library(wordcloud) library(ggplot2) library(data.table) library(rJava) library(RWeka) library(SnowballC)
Views: 5642 ProfessorPitch
Mining Precise positioning Episode Rules  from Event Sequences
 
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TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM ,EMAIL:[email protected] NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Views: 11 NEXGEN TECHNOLOGY
Weka JAVA Data Mining Tool (01)
 
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http://www.zaneacademy.com | Waikato Environment for Knowledge Analysis (Weka) download, install, and test run
Views: 342 ZA Data Mining
Preprocessing, classification, clustering, association rules menggunakan rapid miner
 
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Tugas data mining 3KA08 : byancha, dana, delfi
Views: 71 Delfi Arsita
Omnigram Explorer: Cleveland Heart Disease Data Set (Single Node Brushing mode)
 
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Demonstration of using Omnigram Explorer in Single Node Brushing mode on the Cleveland Heart Disease Data Set to investigate correlations between variables in the data. For more information, see http://www.tim-taylor.com/omnigram
Views: 329 Tim Taylor
Introduction to R Data Analysis: Data Cleaning
 
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Data Cleaning and Dates using lubridate, dplyr, and plyr
Views: 43269 John Muschelli

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