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▶ 5 Most Used Data Mining Software || Data Mining Tools -- Famous Data Mining Tools
 
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»See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on Data Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner Here We're Going to Learn Which Software is best to use in Data Mining Field R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science. আধুনিক প্রযুক্তির ব্যবহার বৃদ্ধির সাথে অতি দ্রুত ডেটা উৎপন্ন হচ্ছে। টেক জায়ান্ট আইবিএম জানায় ইন্টারনেটে যত ডেটা আছে তার ৯০ ভাগ উৎপন্ন হয়েছে গত তিন বছরে। এ ডেটা উৎপন্নের হার দিনকে দিন বেড়েই চলছে। বিশেষজ্ঞদের ধারনা ২০২০ সাল নাগাদ প্রায় ৪০ জেটাবাইট ডেটা জেনারেট হবে। যা ২০১১ তুলনায় প্রায় ৫০ গুন বেশি। বিশাল পরিমাণ এই ডেটা প্রক্রিয়াজাতের মাধ্যমে বিজ্ঞান, গবেষণা, চিকিৎসা, শিক্ষা ও ব্যবসায় ব্যপক ভুমিকা রাখা যেতে পারে। তাই বলা হচ্ছে “ বিগ ডেটা ইজ বিগ ইমপ্যাক্ট।” Data Mining,big data,data analysis,data mining tutorial,book , Bangla tutorials,data mining software,Data Mining,What is data mining, bookbd, data analysis,data mining tutorial,data science,big data,business tutorial,data mining Bangla tutorial,how to,how to mine data,knowledge discovery,Artificial Intelligence,Deep learning,machine learning,Python tutorials,
Views: 4078 BookBd
Orange Data Mining tool
 
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For more information visit orange.biolab.si
Views: 7136 Deeksha Acharya
Open source data mining software and its basic functions
 
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Orange(program) Video made by Wernich Schmidts
Views: 86 Reuben Sousa
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: 415684 Brandon Weinberg
Data Scraping and Mining From Websites Free No Coding Required
 
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In this Parsehub Data scraping begginer tutorial Video, I am going to show you How to Scrap data from almost any website using this simple software. You will Learn to Scrap any website data which is publically available. You don't need to have coding knowledge for web scraping like python, vba script or anything.. There is Absolutely no coding required to scrape any website you want. Software used :Parsehub Free Download Best Data scraper Software Here: Website: https://goo.gl/w232Zm Using This Free Software You Can Easily extract data from any website. Build your own dataset or API, without writing code. You Can Download free Software to scrap data. Steps to scrap the Data: You just need to open the Url of website you want to scrap. Then Just select the data you need. Finally You can Access the data(output file) via JSON, Excel(CSV) and API. Data is collected by their servers. Features:forms, open drop downs, login to websites, click on maps and handle sites with infinite scroll, tabs and pop-ups. Trying to get data from a complex and laggy sites? No worries! You can get data from that websites too. _-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_--_ Special Offers and Deals: _-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_- Best SmartPhones in 2017 Xiaomi Redmi Note 3 - 32 GB (AFFILIATE) http://amzn.to/2ffAcwq Lenovo Phab 2 Plus Smartphone (AFFILIATE) http://amzn.to/2ffDhwv Moto G Plus, 4th Gen 32 GB (AFFILIATE) http://amzn.to/2ffDxeP Lenovo Vibe K4 Note (AFFILIATE) http://amzn.to/2fXUagO ________________________________________________ I hope You liked the video, Please Like, Share.. If you have any comment queries, ask me in comments. SUBSCRIBE This Channel if You Have Not Subscribe Yet.. ♥️ It works for you? Yess then You can donate small donation for my Awesome work..😊 As this channel is non monetized😑 ➡️ Donate ➡️ Paypal: https://bit.ly/2zodsbP
Views: 52475 Just2 techno
A survey of open-source data mining tools - Survey Bots
 
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A survey of open-source data mining tools - Survey Bots
Views: 402 SUMEET KUMAR Singh
Predictive Analytics using Orange Data Mining
 
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Data Mining Fruitful and Fun Open source machine learning and data visualization for novice and expert. Interactive data analysis workflows with a large toolbox. Download Link: https://orange.biolab.si/download/
Views: 345 Anurag P
SPS2017: Educational Data Mining Software
 
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The video is giving details about research software developed using WEKA (Open source Data Mining tool) and JAVA (Programming Language). The first version is developed in 2017. Anyone having the link can download this software and directly use this software without any installation. All the instructions are given in 'README.txt' file in a downloaded zip folder. Any suggestions and questions are invited in the comment section below. Feel free to add below. Music Credits: Youtube Audio Library
How to install Weka | Datamining software on Ubuntu
 
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This video gives the description about how to install WEKA , a opensource datamining software on Ubuntu
Views: 174 Marthutham AI Labs
Open Source Data Analytics: Part of your Standard-Issue Cloud Toolkit
 
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Open-source Big Data technologies have been growing more and more powerful, unlocking a wealth of new possibilities, and Google Cloud Platform is a perfect place to harness these tools with minimal friction. See how these popular open-source technologies interoperate with several different Google Cloud Platform products to let you mix and match these tools effortlessly toward your entrepreneurial goals.
Views: 2679 Google Developers
Blackhat 2012 EUROPE - Data Mining a Mountain of Zero Day Vulnerabilities
 
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This video is part of the Infosec Video Collection at SecurityTube.net: http://www.securitytube.net Blackhat 2012 EUROPE - Data Mining a Mountain of Zero Day Vulnerabilities Every day, software developers around the world, from Bangalore to Silicon Valley, churn out millions of lines of insecure code. We used static binary analysis on thousands of applications submitted to us by large enterprises, commercial software vendors, open source projects, and software outsourcers, to create an anonymized vulnerability data set. By mining this data we can answer some interesting questions. Which industries have the most secure and least secure code? What types of mistakes do developers make most often? Which languages and platforms have the apps with the most vulnerabilities? Should you be most worried of internally built apps, open source, commercial software, or outsourcers? These questions and many more will be answered as we tunnel through zero day mountain. https://media.blackhat.com/bh-eu-12/Wysopal/bh-eu-12-Wysopal-State_of_Software_Security-WP.pdf https://media.blackhat.com/bh-eu-12/Wysopal/bh-eu-12-Wysopal-State_of_Software_Security-Slides.pdf
Views: 1405 SecurityTubeCons
Answers from Big Data - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 254 Data Analytics
Executing Open Source Code in Machine Learning Pipelines
 
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Executing Open Source Code in Machine Learning Pipelines of SAS Visual Data Mining and Machine Learning http://support.sas.com/software/products/visual-data-mining-machine-learning/index.html Presenter: Radhikha Myneni Radhikha Myneni demonstrates how to execute open source code, specifically Python and R in SAS Visual Data Mining & Machine Learning pipelines. 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: 285 SAS Software
Harvest - Open Source Browser Mining Software
 
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Harvest is an Open Source Monero Browser Mining Application. The Mission is to provide a Community Browser(Javascript/WebAssembly) Mining Software for the Monero.
Views: 980 Kames Cox-Geraghty
Tanagra Data Mining
 
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an "open source project" as every researcher can access to the source code, and add his own algorithms, as far as he agrees and conforms to the software distribution license.
Views: 13504 Emmanuel Felipe
Introduction to Weka
 
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This video cover Introduction to Weka: A Data Mining Tool. This tool is open source, freely available, very light and Java based. It can be used to apply data mining algorithms very easily by using simple GUI.
SEO - Keyword discovery tool - Mozenda Data Mining - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 72 Data Analytics
RapidMiner Tutorial - GUI Overview (Data Mining and Predictive Analytics Software)
 
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A tutorial overview of the RapidMiner GUI. RapidMiner is an open source system for data mining, predictive analytics, machine learning, and artificial intelligence applications. For more information: http://rapid-i.com/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/
Views: 3405 Predictive Analytics
Overview of Open Source Data Mining Tools: AWS, Hadoop, Apache Spark, Clusters, NoSQL
 
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Adapted from Openwest and Intermountain Big Data conference talks about NoSql, Java, Machine Learning, Python, Data Mining and the myriad tools you need to know exist. Subject covered: Development environments -- why you need to know more tools than just a language Amazon AWS -- talk about free tier and look at management panel This will cover the basics of what an aspiring data scientist needs to know. The whole point of this is to give an overview of many different technologies. Your first battle in data science is simply knowing what exists and how it all relates. This is not a deep dive, it's an overview, so don't worry about taking notes, just grab a coffee and join me online for a whirlwind tour. This is going to be a do-over of the first half of a talk I gave at the Big Data conference a few weeks ago. I did not go a good job presenting my slides to the Google Hangouts viewers and will re-present this. The talks this is based on (this will be the first half, not covereing Shiny until a future Hangout on iPython/Jupyter and Shiny tbd) Utah Intermountain Big Data Conference Data Science and Machine Learning Tools from Python to R, with Hands-On R/Shiny project http://utahgeekevents.com/Sessions (click on "Data Technologies for Developers") Openwest 2015 "Hadoop, MapReduce, Weka & Python Pandas, Oh My? A Data Mining and Machine Learning Primer" http://www.openwest.org/schedule/#68
Views: 1035 Mega Learning
KEEL Data mining tool demo
 
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KEEL Data minig tool Demo of installation and Working
Views: 3462 Manukumar K J
Efficient Data Mining for Personalized Medicine
 
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Increasingly, patient care will rely on integration of highly complex and multifaceted clinical and molecular data, such as clinical genomics and other OMICS data. The enormous volume of accumulated data has challenged software developers, scientists, and clinicians because investigators and their work are separated by therapeutic area and or developmental stage, location of data in large internal siloes, and the need to efficiently interpret and analyze data in clinically useful ways. Integration of internal knowledge with external data from open and commercial sources has also proved challenging. The tranSMART Knowledge Management Platform, an open-source knowledge-management platform, has allowed medical and life science researchers to advance translational science and clinical research through information sharing and collaboration. By combining a data repository with intuitive search capabilities and analysis tools, the tranSMART system provides researchers a single self-service web portal with access to phenotypic, omics, and unstructured text-based data from multiple internal and external sources. tranSMART can, for example, help scientists develop and refine research hypotheses by investigating correlations between genetic and phenotypic data, and assessing their analytical results in the context of published literature and other work. Optimal use of tranSMART may require substantial expertise in life sciences IT, bioinformatics, semantics, and biology. During this webinar, experts from tranSMART and Thomson Reuters will present features of the tranSMART platform, and describe services offered by Thomson Reuters that can facilitate full and efficient use of the platform through consulting services in installation and IT support, data annotation and data mining as well as application plug-ins. The integration of tranSMART into a data exchange portal at a major public private partnerships will also be described.
Views: 864 GENNews
Kamanja: An Open Source Real Time System for Scoring Data Mining Models, Greg Makowski 20150727
 
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Greg Makowski, Director of Data Science, LigaDATA This talk will start with a number of complex data real-time use cases, such as a) complex event processing, b) supporting the modeling of a data mining department and c) developing enterprise applications on Apache big-data systems. While Hadoop and big data has been around for a while, banks and healthcare companies tend not to be early IT adopters. What are some of the security or roadblocks in Apache big data systems for such industries with high requirements? Data mining models can be trained in dozens of packages, but what can simplify the deployment of models regardless of where they were trained or with what algorithm? Predictive Modeling Markup Language (PMML), is a type of XML with specific support for 15 families of data mining algorithms. Data mining software such as R, KNIME, Knowledge Studio, SAS Enterprise Miner are PMML producers. The new open-source product, Kamanja, is the first open-source, real-time PMML consumer (scoring system). One advantage of PMML systems is that it can reduce time to deploy production models from 1-2 months to 1-2 days - a pain point that may be less obvious if your data mining exposure is competitions or MOOCs. Kamanja is free on Github, supports Kafka, MQ, Spark, HBase and Cassandra among other things. Being a new open-source product, initially, Kamanja supports rules, trees and regression. I will cover an architecture of a sample application using multiple real-time open source data, such as social network campaigns and tracking sentiment for the bank client and its competitors. Other real-time architectures cover credit card fraud detection. A brief demo will be given of the social network analysis application, with text mining. An overview of products in the space will include popular Apache big data systems, real-time systems and PMML systems. For more details: Slides: http://www.slideshare.net/gregmakowski/kamanja-driving-business-value-through-realtime-decisioning-solutions http://kamanja.org/ http://www.meetup.com/SF-Bay-ACM/events/223615901/ http://www.sfbayacm.org/event/kamanja-new-open-source-real-time-system-scoring-data-mining-models Venue sponsored by eBay, Food and live streaming sponsored by LigaDATA, San Jose, CA, July 27, 2015 Chapter Chair Bill Bruns Data Science SIG Program Chair Greg Makowski Vice Chair Ashish Antal Volunteer Coordinator Liana Ye Volunteers Joan Hoenow, Stephen McInerney, Derek Hao, Vinay Muttineni Camera Tom Moran Production Alex Sokolsky Copyright © 2015 ACM San Francisco Bay Area Professional Chapter
Free Machine Learning Software | Machine Learning Software | Free Machine Learning Software
 
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Free Machine Learning Software: http://bit.ly/2gUOYKI Free Machine Learning Software for you. This software is totally free. Just click the upper link and download the Machine Learning Software Free. Top searching keywords: Free Machine Learning Software | machine learning software | data mining software | weka software | mining software | data mining tools | open source machine learning software | weka | machine learning with r | introduction to machine learning | machine learning software open source | data extraction software | machine learning software tools | machine learning open source software | download weka tool | artificial intelligence software | machine learning online course | weka data mining
Views: 43 Jamie Lopez
Data Mining with WEKA
 
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Learn Data Mining with WEKA - Open Source Data Mining and Machine Learning platform Software: http://www.cs.waikato.ac.nz/ml/weka/ More about FX Data Mining: http://www.algonell.com/
[LIVE] Kamanja: A New Open Source Real-Time System for Scoring Data Mining Models, Greg Makowski,
 
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[Streamed version. Front & back trimmed. Slide issue in beginning.] An edited version is available: https://www.youtube.com/watch?v=ANqB72b0r38 Slides: http://www.slideshare.net/gregmakowski/kamanja-driving-business-value-through-realtime-decisioning-solutions Greg Makowski, Director of Data Science, LigaDATA This talk will start with a number of complex data real-time use cases, such as a) complex event processing, b) supporting the modeling of a data mining department and c) developing enterprise applications on Apache big-data systems. While Hadoop and big data has been around for a while, banks and healthcare companies tend not to be early IT adopters. What are some of the security or roadblocks in Apache big data systems for such industries with high requirements? Data mining models can be trained in dozens of packages, but what can simplify the deployment of models regardless of where they were trained or with what algorithm? Predictive Modeling Markup Language (PMML), is a type of XML with specific support for 15 families of data mining algorithms. Data mining software such as R, KNIME, Knowledge Studio, SAS Enterprise Miner are PMML producers. The new open-source product, Kamanja, is the first open-source, real-time PMML consumer (scoring system). One advantage of PMML systems is that it can reduce time to deploy production models from 1-2 months to 1-2 days - a pain point that may be less obvious if your data mining exposure is competitions or MOOCs. Kamanja is free on Github, supports Kafka, MQ, Spark, HBase and Cassandra among other things. Being a new open-source product, initially, Kamanja supports rules, trees and regression. I will cover an architecture of a sample application using multiple real-time open source data, such as social network campaigns and tracking sentiment for the bank client and its competitors. Other real-time architectures cover credit card fraud detection. A brief demo will be given of the social network analysis application, with text mining. An overview of products in the space will include popular Apache big data systems, real-time systems and PMML systems. For more details: http://kamanja.org/ http://www.meetup.com/SF-Bay-ACM/events/223615901/ http://www.sfbayacm.org/event/kamanja-new-open-source-real-time-system-scoring-data-mining-models Venue sponsored by eBay, Food and live streaming sponsored by LigaDATA, San Jose, CA, July 27, 2015 Chapter Chair Bill Bruns Data Science SIG Program Chair Greg Makowski Vice Chair Ashish Antal Volunteer Coordinator Liana Ye Volunteers Joan Hoenow, Stephen McInerney, Derek Hao, Vinay Muttineni Camera Tom Moran Production Alex Sokolsky Copyright © 2015 ACM San Francisco Bay Area Professional Chapter
Views: 1146 San Francisco Bay ACM
Rattle for Data Mining - Using R without programming (CRAN)
 
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www.learnanalytics.in demostrates use of an free and open source platform to build sophisticated predictive models. We demonstrate using R package Rattle to do data analysis without writing a line of r code. We cover hypothesis testing, descriptive statistics, linear and logistic regression with a flavor of machine learning (Random Forest, SVM etc.). Also using graphs such as ROC curves and Area under curves (AUC) to compare various models. To download the dataset and follow on your own follow http://www.learnanalytics.in/datasets/Credit_Scoring.zip
Views: 41168 Learn Analytics
GNU PSPP For Linux Mint ( Ubuntu) :  A Free & Open Source Alternative To IBM SPSS
 
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GNU PSPP For Linux Mint ( ubuntu) : A Free & Open Source Alternative To IBM SPSS ( Statistical analysis and data mining tool) Installation Command: sudo apt-get install pspp ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ # Visit my blog for more updates - http://linuxforever.info/ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ ❤ PSPP is a program for statistical analysis of sampled data. It is a free replacement for the proprietary program SPSS. PSPP supports T-tests, ANOVA, GLM, factor analysis, non-parametric tests, and other statistical features. PSPP produces statistical reports in plain text, PDF, PostScript, CSV, HTML, SVG, and OpenDocument formats. PSPP has both text-based and graphical user interfaces. The PSPP user interface has been translated into a number of languages.
Views: 3870 linuxforever
how to install elki data mining on ubuntu 16.10
 
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ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI offers data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions of additional methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms. source link:-https://elki-project.github.io/ -------------------------------------------------------------------------------------------------- commands: apt-get update apt-get install elki -------------------------------------------------------------------------------------------------- ELKI: Environment for Developing KDD-Applications Supported by Index-Structures --------------------------------------------------------------------------------------------------
Views: 468 Tech ind
40 Data Analysis New Tools - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 84 Data Analytics
Data Mining mit Orange
 
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Andreas Bresser https://2013.de.pycon.org/schedule/sessions/3/ Es wird eine einfache Einführung in das Thema Data Mining gegeben. Dazu werden anschauliche Beispiele für den Einsatz von Orange gezeigt. Orange ist ein Open Source Programm, das Data Mining und Datenvisualisierung durch visuelle Programmierung oder Python Scripting ermöglicht.
Views: 1416 Next Day Video
Searching and Mining Open Source Code from the Web
 
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Google Tech Talks June, 4 2008 ABSTRACT Various data mining techniques have been applied to mine source code repositories. However, relying only on one or several local source code repositories may not provide sufficient, relevant data samples (e.g., usage of a certain API call) for mining tasks such as code reuse and defect detection. The recent availability of code search engines allows the mining scope to be scaled to billions of lines of open source code available from the Web, and thus increases the chance of getting sufficient, relevant data samples for mining. This talk will discuss the mining opportunities and challenges based on searching open source code from the Web and present new approaches that mine open source code searched from the Web to assist code reuse and defect detection Speaker: Tao Xie Tao Xie is an Assistant Professor in the Department of Computer Science at North Carolina State University. He received his Ph.D. in Computer Science from the University of Washington in 2005. He leads the Automated Software Engineering Research Group at North Carolina State University. His research centers around two major themes: automated software testing and mining software engineering data. He has served on a number of conference program committees including ISSTA 2008/2009, ASE 2006/2007(Expert-Review Panel)/2008, ICST 2008, AOSD 2007, and ICSM 2007/2008. Besides doing research, he has contributed to understanding the software engineering research community by building community webs such as Software Engineering Academic Genealogy and Software Engineering Conference Map.
Views: 5780 GoogleTechTalks
Opensource BI tool-Pentaho
 
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http://www.pragtech.co.in/services/business-intelligence/pentaho.html The Pentaho Business Analytics platform provides enterprise-class reporting, analysis, dashboard, data mining and work flow capabilities that help enterprises operate more efficiently and effectively. The software offers a very flexible deployment options such as embeddable components, customized BI application solutions, and as a complete out-of-the-box, integrated BI platform.
An Introduction to KNIME
 
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This video is an introduction of KNIME. KNIME is an open source platform for data analysis, predictive analytics and modeling. It is not based on a script language rather it has a graphical interface. This video shows the basic functions KNIME in terms of the process of reading, manipulating, visualizing and analyzing data.
Views: 94952 KNIMETV
No coding required: Expand your data mining toolset with predictive extensions
 
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Open-source technology is creating new opportunities to become data-driven. For years, users of IBM SPSS Modeler have incorporated open-source languages within predictive models to conduct analysis. Now, take this capability a step farther with the introduction of predictive extensions, which can be plugged into SPSS Modeler to allow professional analysts and business users to take advantage of additional functionality without having to write code. Find out how enhancements to SPSS Modeler will enable you to do the following: • Find predictive extensions with the help of the SPSS predictive analytics community. • Download, install and modify predictive extensions through the SPSS Modeler interface. • Build your own R- and Python-based predictive extensions using the Custom Dialog Builder. Learn how you can use SPSS predictive extensions to take advantage of open source technology in your data analysis. Learn more about IBM SPSS http://ibm.co/spsstrial Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata The world is becoming smarter every day, join the conversation on the IBM Big Data & Analytics Hub: http://www.ibmbigdatahub.com https://www.youtube.com/user/ibmbigdata https://www.facebook.com/IBManalytics https://www.twitter.com/IBMbigdata https://www.slideshare.net/IBMBDA
Views: 289 IBM Analytics
Open Source Big Data Mining Tool 'ankus' Demo/Manual
 
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Contact Us for more information to www.openankus.org
Views: 220 Ankus Community
How To Connect Google Webmaster Tools To Google Analytics - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 81 Data Analytics
Mining Software Repository Made Easy - Boa Language and its Data Store
 
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Software repositories, e.g. SourceForge, GitHub, etc. contain an enormous corpus of software and information about software. Scientists and engineers alike are interested in analyzing this wealth of information both for curiosity as well as for testing important research hypotheses. However, the current barrier to entry is prohibitive and the cost of such scientific experiments great. Furthermore, these experiments are often irreproducible. This talk will describe our work on the Boa language and its data-intensive infrastructure. In a nutshell, Boa aims to be for open source-related research as Mathematica is to numerical computing, R is for statistical computing, and Verilog and VHDL is for hardware description. Our evaluation shows that Boa significantly decreases the burden of the scientists and engineers analyzing human and technical aspects of open source software development allowing them to focus on the essential tasks of scientific research. This is a collaborative work with Robert Dyer, Hoan Nguyen and Tien Nguyen all at Iowa State University.
Views: 529 Microsoft Research
RapidMiner Tutorial - Evaluation  (Data Mining and Predictive Analytics System)
 
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A tutorial discussing analytics evaluation with RapidMiner, an open source system for data mining, predictive analytics, machine learning, and artificial intelligence applications. For more information: http://rapid-i.com/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/
Views: 4742 Predictive Analytics
Building Bitcoin Software From Source Code
 
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Presentation Slides: http://averageradical.github.io/compile/#/ Send Tips directly to KevG @ bitcoin:1QDEf7xr33aHGPZUHg9WHQkyGLcYKXcv4i Much of the software associated with Bitcoin is open source, i.e. wallets, address generators, mining software, operating systems... Open source means the raw computer code is publicly available for scrutiny. Open source software is usually considered less buggy and more secure. One common obstacle with open source software is transforming it from the raw computer code to a program that runs on your phone, tablet, laptop, desktop. This process is called 'building from source'. This presentation seeks to demonstrate how to build from source several useful applications, with the ability to run these programs on multiple operating systems. Additionally, we will dive into the source code to make small changes prior to building, i.e. background color, edit text, etc... No software development experience necessary.
Views: 23069 Edge
How To Install KNIME Analytics Platform on Windows, Installation and Administration Guide
 
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This video is an introduction of KNIME. KNIME is an open source platform for data analysis, predictive KNIME, the open platform for your data. ... Course for KNIME Analytics Platform, Berlin - November 2017. 15 Nov 2017. Course for KNIME Server, Berlin KNIME (pronounced /naɪm/), the Konstanz Information Miner, is an open source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. A graphical user interface allows assembly of nodes for data preprocessing (ETL: Extraction, Transformation, Loading), for modeling and data analysis and visualization without, or with only minimal, programming. To some extent KNIME can be considered as an SAS alternative. Since 2006, KNIME has been used in pharmaceutical research,[2] but is also used in other areas like CRM customer data analysis, business intelligence and financial data analysis. History: The Development of KNIME was started January 2004 by a team of software engineers at University of Konstanz as a proprietary product. The original developer team headed by Michael Berthold came from a company in Silicon Valley providing software for the pharmaceutical industry. The initial goal was to create a modular, highly scalable and open data processing platform which allowed for the easy integration of different data loading, processing, transformation, analysis and visual exploration modules without the focus on any particular application area. The platform was intended to be a collaboration and research platform and should also serve as an integration platform for various other data analysis projects. In 2006 the first version of KNIME was released and several pharmaceutical companies started using KNIME and a number of life science software vendors began integrating their tools into KNIME. Later that year, after an article in the German magazine c't users from a number of other areas[9][10] joined ship. As of 2012, KNIME is in use by over 15,000 actual users (i.e. not counting downloads but users regularly retrieving updates when they become available) not only in the life sciences but also at banks, publishers, car manufacturer, telcos, consulting firms, and various other industries but also at a large number of research groups worldwide. Latest updates to KNIME Server and KNIME Big Data Extensions, provide support for Apache Spark 2.0. Internals KNIME allows users to visually create data flows (or pipelines), selectively execute some or all analysis steps, and later inspect the results, models, and interactive views. KNIME is written in Java and based on Eclipse and makes use of its extension mechanism to add plugins providing additional functionality. The core version already includes hundreds of modules for data integration (file I/O, database nodes supporting all common database management systems through JDBC), data transformation (filter, converter, combiner) as well as the commonly used methods of statistics, data mining, analysis and text analytics. Visualization supports with the free Report Designer extension. KNIME workflows can be used as data sets to create report templates that can be exported to document formats like doc, ppt, xls, pdf and others. Other capabilities of KNIME are: KNIMEs core-architecture allows processing of large data volumes that are only limited by the available hard disk space (most other open source data analysis tools work in main memory and are therefore limited to the available RAM). E.g. KNIME allows analysis of 300 million customer addresses, 20 million cell images and 10 million molecular structures. Additional plugins allows the integration of methods for Text mining, Image mining, as well as time series analysis. KNIME integrates various other open-source projects, e.g. machine learning algorithms from Weka, the statistics package R project, as well as LIBSVM, JFreeChart, ImageJ, and the Chemistry Development Kit. KNIME is implemented in Java but also allows for wrappers calling other code in addition to providing nodes that allow running Java, Python, Perl and other code fragments. also a ML tool is WEKA(A Data Mining Tool)
Views: 129 Hammad Zafar
Building a Real Time Fraud Prevention Engine Using Open Source Big Data: by Keesjan de Vries
 
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Fraudsters attempt to pay for goods, flights, hotels – you name it – using stolen credit cards. This hurts both the trust of card holders and the business of vendors around the world. We built a Real-Time Fraud Prevention Engine using Open Source (Big Data) Software: Spark, Spark ML, H2O, Hive, Esper. In my talk I will highlight both the business and the technical challenges that we’ve faced and dealt with.
Views: 2727 Spark Summit
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 805753 David Langer
RapidMiner 5 Tutorial - Video 3 - Creating a process
 
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Vancouver Data Blog http://vancouverdata.blogspot.com/ This is a RapidMiner tutorial. Rapid Miner (formerly Yale) is an open source java data and text mining program, similar to Weka, free to download. Other rapidminer examples are available on my channel.
Views: 9270 el chief
R and Bioconductor: open source software for analysing genomic data
 
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Belinda Phipson https://linux.conf.au/schedule/30307/view_talk http://afrubin.github.io/miniconf/
Ankus (Data Mining and Machine Learning) Open Source User Guide Video
 
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하둡 기반의 Ankus (Data Mining and Machine Learning) 오픈소스의 전체 기능을 가이드한 동영상입니다.
Views: 407 전수현
04 Predictive Analytics Training with Weka (Building a classifier)
 
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Data Mining and Predictive Analytics training course using the open source Weka tool. Videos producted by the University of Waikato, New Zealand. Posted by Rapid Progress Marketing and Modeling, LLC (RPM2) under CC BY 3.0 RPM2 is a full-service Predictive Analytics and Data Sciences Services company specializing in Model Development, Consulting, Direct Marketing Services, and Professional Training. Visit us at http://www.RPMSquared.com/
Views: 15414 Predictive Analytics
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 324237 edureka!
3.3 Machine learning softwares
 
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Software suites containing a variety of machine learning algorithms: – Open source software – Proprietary software – Proprietary software with open source editions Open source softwares – CNTK (Microsoft Cognitive Toolkit) – TensorFlow ELKI H2O Caffe – Mahout Mallet MLPACK MXNet – OpenNN Orange scikit-learn Shogun – Weka/MOA Yooreeka Deeplearning4j – Spark Mllib Torch / PyTorch Apache Singa Proprietary software - Open-source editions • KNIME • RapidMiner KNIME, the Konstanz Information Miner, is a open-source data analytics, reporting and integration platform. – KNIME integrates various components for machine learning and data mining. – KNIME Software in available in the Cloud – Azure and AWS. RapidMiner is a data science platform that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. – It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process. Proprietary software • Amazon Machine Learning • Angoss Knowledge STUDIO • Ayasdi • IBM Data Science Experience • Google Prediction API • IBM SPSS Modeler • KXEN Modeler • LIONsolver • Mathematica • MATLAB • Microsoft Azure ML • Neural Designer • Splunk • Oracle Data Mining • SAP Leonardo ML • SAS Enterprise Miner AWS and Machine Learning • Amazon is investing in artificial intelligence for over 20 years. Echo powered by Alexa and others are just the beginning. • Amazon mission is to share ML capabilities as fully managed services.
Views: 20 CBTUniversity