SlideShare a Scribd company logo

Data mining tools overall

•Download as PPTX, PDF•
•
Mohamed Sharique Vellikan
Mohamed Sharique Vellikan

Orange, R, RapidMiner, and WEKA are open source data mining and machine learning tools. Orange has an elegant scripting interface and can be run in GUI or ETL mode. R has elegant scripting integrated with extensive statistical libraries. RapidMiner has many features and good connectivity. WEKA has the easiest GUI but more limited connectivity than the other tools. The document compares the tools on factors such as supported data formats, user interfaces, connectivity, and provides examples of companies using the different tools.

Data & Analytics
1 of 16
Data Mining Tools Kowshik Madhumati Mayur Mohamed Sharique Vidyashankar
• Open source • Data visualization and analysis • Novice and experts • Through Python scripting • Available for all popular platforms, including Windows, Mac OS X and variants of Linux. • Founded on 1996 • Orange is distributed free under the GPL. • M&D at the Bioinformatics Laboratory of the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. Product Details Company Details Python is a widely used general-purpose, high-level programming language. GNU General Public License is the most widely used free software license
Features • Visual Programming • Visualization • Interaction and Data Analytics • Large Toolbox • Scripting Interface • Extendable • Documentation • Open Source • Platform Independence Success Stories • Astra-Zeneca, a pharmaceutical giant, which uses Orange in drug development and sponsors the development of several related parts of Orange • At Jožef Stefan Institute, the visual programming interface has been upgraded in Orange4WS to support service-oriented architectures
Screenshot
• Latest R-language engine for statistical computing • Open source, R- Enterprise, R-Cloud(Paid version ) • Data visualization and analysis up to 16 TB • Extended capabilities with reproducible R tool Kits • Windows , Mac OS and variants of Linux. • Founded on 1993 in New Zealand • Robert and Rossa pioneer in R language development . • R has General Public Licence. • Many Big MNC companies are using R software. Product Details Company Details
Useful Functions • Graphics Visualization • Spatial Data Analysis • Clustering • Text Mining • Social Network Analysis and Graph mining • Statistics • Data Manipulation Success Stories • Bank of America • Bing • Facebook • Ford • Google
Screenshot
• Open source • a collection of machine learning algorithms • Data visualization and analysis • Java based platform • Most researchers and practitioners • Founded on 1997 • University of Waikato Product Details Company Details Public License is the most widely used free software license
Features • General public license • GUI for interacting • Explorer is the main user interface of WEKA • primitive tasks including data pre-processing, classification, regression, clustering, association rules and visualization • Execute data files in multiple format • One exceptional feature of WEKA is the database connection using JDBC with any RDBMS package • The Weka mailing list has over 1100 subscribers in 50 countries, including subscribers from many major companies such as Rechtsportal Success Stories
Screenshot
• Open source. • Data visualization and analysis • Machine Learning • Data Mining, Text Mining. • Business Intelligence. • Works on java runtime. • Available on all major operating systems and platforms • Started as YALE in 2001 by Ralf Klinkenberg, Ingo Mierswa, and Simon Fische • In 2006 it was renamed by Rapidminer since developed by Rapid-1 founded by Ralf Klinkenberg, Ingo Mierswa • Licensed by AGPL. Product Details Company Details
Features • A visual - code-free - environment, so no programming needed • Design of analysis processes • Predictive analytics (with pre-made templates) • Data loading • Data transformation • Data Modelling • Data visualization (with lots of visualizations) • Allows you to work with different types and sizes of data sources • Platform Independence. • Acts as a powerful scripting language engine along with a graphical user • Modular operator concept. • CISCO • PAYPAL • EBAY • MIELE • VOLKSWAGEN Success Stories
Screenshot
COMPARISON OF ALL TOOLS WEKA RAPIDMINER R- PROGRAMMING ORANGE FORMATS SUPPORTED ONLY 4 FILE FORMATS ARE SUPPORTED SUPPORTS MORE FILE FORMATS (Approx 22) SUPPORTS MORE FILE FORMATS SUPPORTS MORE FILE FORMATS USER INTERFACE EASY USER INTERFACE DIFFICULT USER INTERFACE SIMPLE IN UNIX OS,DIFFICULT IN WINDOWS AND MAC EASY CONNECTIVITY WORSE CONNECTIVITY WITH EXCEL AND NON JAVA DATABASES EASILY CONNECTED WITH EXCEL EASY CONNECTIVITY WITH EXCEL AND OTHER DATABASES BETTER THAN WEKA
Orange has elegant and concise scripting and can also be run in an ETL GUI mode. R has elegant and concise scripting integrated with a vast statistical library. RapidMiner has a lot of functionality, is polished and has good connectivity. WEKA is the easiest GUI to learn and use.
• http://old.biolab.si/ • http://en.wikipedia.org/ • http://www.predictiveanalyticsto day.com/ • http://thenewstack.io/ • www.facebook.com/ • www.slideshare.net/ • www.kdnuggets.com/ • www.researchgate.net • https://rapidminer.com/ • www.r-project.org • sourceforge.net/projects/weka • www.thearling.com

Recommended

Mapreduce Algorithms
Mapreduce AlgorithmsMapreduce Algorithms
Mapreduce AlgorithmsAmund Tveit
 
Building Recommender Systems for Fashion
Building Recommender Systems for FashionBuilding Recommender Systems for Fashion
Building Recommender Systems for FashionNick Landia
 
Data preprocessing using Machine Learning
Data preprocessing using Machine Learning Data preprocessing using Machine Learning
Data preprocessing using Machine Learning Gopal Sakarkar
 
data generalization and summarization
data generalization and summarization data generalization and summarization
data generalization and summarization janani thirupathi
 
Big Data Analytics
Big Data AnalyticsBig Data Analytics
Big Data AnalyticsGhulam Imaduddin
 
CRISP-DM: a data science project methodology
CRISP-DM: a data science project methodologyCRISP-DM: a data science project methodology
CRISP-DM: a data science project methodologySergey Shelpuk
 
Analytical tools
Analytical toolsAnalytical tools
Analytical toolsAniket Joshi
 
Applications of Machine Learning
Applications of Machine LearningApplications of Machine Learning
Applications of Machine LearningHayim Makabee
 

Recommended

Mapreduce Algorithms
Mapreduce AlgorithmsMapreduce Algorithms
Mapreduce AlgorithmsAmund Tveit
 
Building Recommender Systems for Fashion
Building Recommender Systems for FashionBuilding Recommender Systems for Fashion
Building Recommender Systems for FashionNick Landia
 
Data preprocessing using Machine Learning
Data preprocessing using Machine Learning Data preprocessing using Machine Learning
Data preprocessing using Machine Learning Gopal Sakarkar
 
data generalization and summarization
data generalization and summarization data generalization and summarization
data generalization and summarization janani thirupathi
 
Big Data Analytics
Big Data AnalyticsBig Data Analytics
Big Data AnalyticsGhulam Imaduddin
 
CRISP-DM: a data science project methodology
CRISP-DM: a data science project methodologyCRISP-DM: a data science project methodology
CRISP-DM: a data science project methodologySergey Shelpuk
 
Analytical tools
Analytical toolsAnalytical tools
Analytical toolsAniket Joshi
 
Applications of Machine Learning
Applications of Machine LearningApplications of Machine Learning
Applications of Machine LearningHayim Makabee
 
Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data WarehousingEyad Manna
 
Types of Machine Learning
Types of Machine LearningTypes of Machine Learning
Types of Machine LearningSamra Shahzadi
 
Activation function
Activation functionActivation function
Activation functionAstha Jain
 
Text MIning
Text MIningText MIning
Text MIningPrakhyath Rai
 
Knowledge discovery thru data mining
Knowledge discovery thru data miningKnowledge discovery thru data mining
Knowledge discovery thru data miningDevakumar Jain
 
What is ETL?
What is ETL?What is ETL?
What is ETL?Ismail El Gayar
 
Introduction to Data Engineering
Introduction to Data EngineeringIntroduction to Data Engineering
Introduction to Data EngineeringVivek Aanand Ganesan
 
Getting a Data Science Job
Getting a Data Science JobGetting a Data Science Job
Getting a Data Science JobAlexey Grigorev
 
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine LearningShahar Cohen
 
Database Performance Tuning
Database Performance Tuning Database Performance Tuning
Database Performance Tuning Arno Huetter
 
Document Database
Document DatabaseDocument Database
Document DatabaseHeman Hosainpana
 
Kdd process
Kdd processKdd process
Kdd processRajesh Chandra
 
Machine learning and types
Machine learning and typesMachine learning and types
Machine learning and typesPadma Metta
 
OLAP & DATA WAREHOUSE
OLAP & DATA WAREHOUSEOLAP & DATA WAREHOUSE
OLAP & DATA WAREHOUSEZalpa Rathod
 
Machine Learning: Introduction to Neural Networks
Machine Learning: Introduction to Neural NetworksMachine Learning: Introduction to Neural Networks
Machine Learning: Introduction to Neural NetworksFrancesco Collova'
 
Ppt
PptPpt
Pptbullsrockr666
 
Data mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, ClassificationData mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
 
Knowledge representation In Artificial Intelligence
Knowledge representation In Artificial IntelligenceKnowledge representation In Artificial Intelligence
Knowledge representation In Artificial IntelligenceRamla Sheikh
 
05 Clustering in Data Mining
05 Clustering in Data Mining05 Clustering in Data Mining
05 Clustering in Data MiningValerii Klymchuk
 
Data mining tools used in business intelligence
Data mining tools used in business intelligenceData mining tools used in business intelligence
Data mining tools used in business intelligenceNithya Ravi
 
Data mining tools
Data mining toolsData mining tools
Data mining toolssuganmca14
 

More Related Content

What's hot

Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data WarehousingEyad Manna
 
Types of Machine Learning
Types of Machine LearningTypes of Machine Learning
Types of Machine LearningSamra Shahzadi
 
Activation function
Activation functionActivation function
Activation functionAstha Jain
 
Knowledge discovery thru data mining
Knowledge discovery thru data miningKnowledge discovery thru data mining
Knowledge discovery thru data miningDevakumar Jain
 
Getting a Data Science Job
Getting a Data Science JobGetting a Data Science Job
Getting a Data Science JobAlexey Grigorev
 
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine LearningShahar Cohen
 
Database Performance Tuning
Database Performance Tuning Database Performance Tuning
Database Performance Tuning Arno Huetter
 
Machine learning and types
Machine learning and typesMachine learning and types
Machine learning and typesPadma Metta
 
OLAP & DATA WAREHOUSE
OLAP & DATA WAREHOUSEOLAP & DATA WAREHOUSE
OLAP & DATA WAREHOUSEZalpa Rathod
 
Machine Learning: Introduction to Neural Networks
Machine Learning: Introduction to Neural NetworksMachine Learning: Introduction to Neural Networks
Machine Learning: Introduction to Neural NetworksFrancesco Collova'
 
Data mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, ClassificationData mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, ClassificationDr. Abdul Ahad Abro
 
Knowledge representation In Artificial Intelligence
Knowledge representation In Artificial IntelligenceKnowledge representation In Artificial Intelligence
Knowledge representation In Artificial IntelligenceRamla Sheikh
 
05 Clustering in Data Mining
05 Clustering in Data Mining05 Clustering in Data Mining
05 Clustering in Data MiningValerii Klymchuk
 

What's hot (20)

Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data Warehousing
 
Types of Machine Learning
Types of Machine LearningTypes of Machine Learning
Types of Machine Learning
 
Activation function
Activation functionActivation function
Activation function
 
Text MIning
Text MIningText MIning
Text MIning
 
Knowledge discovery thru data mining
Knowledge discovery thru data miningKnowledge discovery thru data mining
Knowledge discovery thru data mining
 
What is ETL?
What is ETL?What is ETL?
What is ETL?
 
Introduction to Data Engineering
Introduction to Data EngineeringIntroduction to Data Engineering
Introduction to Data Engineering
 
Getting a Data Science Job
Getting a Data Science JobGetting a Data Science Job
Getting a Data Science Job
 
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
 
Database Performance Tuning
Database Performance Tuning Database Performance Tuning
Database Performance Tuning
 
Document Database
Document DatabaseDocument Database
Document Database
 
Kdd process
Kdd processKdd process
Kdd process
 
Machine learning and types
Machine learning and typesMachine learning and types
Machine learning and types
 
OLAP & DATA WAREHOUSE
OLAP & DATA WAREHOUSEOLAP & DATA WAREHOUSE
OLAP & DATA WAREHOUSE
 
Machine Learning: Introduction to Neural Networks
Machine Learning: Introduction to Neural NetworksMachine Learning: Introduction to Neural Networks
Machine Learning: Introduction to Neural Networks
 
Ppt
PptPpt
Ppt
 
Data mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, ClassificationData mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, Classification
 
Knowledge representation In Artificial Intelligence
Knowledge representation In Artificial IntelligenceKnowledge representation In Artificial Intelligence
Knowledge representation In Artificial Intelligence
 
05 Clustering in Data Mining
05 Clustering in Data Mining05 Clustering in Data Mining
05 Clustering in Data Mining
 

Viewers also liked

Data mining tools used in business intelligence
Data mining tools used in business intelligenceData mining tools used in business intelligence
Data mining tools used in business intelligenceNithya Ravi
 
Data mining tools
Data mining toolsData mining tools
Data mining toolssuganmca14
 
A comparative analysis of data mining tools for performance mapping of wlan data
A comparative analysis of data mining tools for performance mapping of wlan dataA comparative analysis of data mining tools for performance mapping of wlan data
A comparative analysis of data mining tools for performance mapping of wlan dataIAEME Publication
 
Slides PAPIs.io'14 RapidMiner
Slides PAPIs.io'14 RapidMinerSlides PAPIs.io'14 RapidMiner
Slides PAPIs.io'14 RapidMinerSabrina Kirstein
 
Hadoop World 2011: Radoop: a Graphical Analytics Tool for Big Data - Gabor Ma...
Hadoop World 2011: Radoop: a Graphical Analytics Tool for Big Data - Gabor Ma...Hadoop World 2011: Radoop: a Graphical Analytics Tool for Big Data - Gabor Ma...
Hadoop World 2011: Radoop: a Graphical Analytics Tool for Big Data - Gabor Ma...Cloudera, Inc.
 
M Chambers and RapidMiner Overview for Babson class
M Chambers and RapidMiner Overview for Babson classM Chambers and RapidMiner Overview for Babson class
M Chambers and RapidMiner Overview for Babson classmcAnalytics99
 
RapidMiner, an entrance to explore MIMIC-III?
RapidMiner, an entrance to explore MIMIC-III?RapidMiner, an entrance to explore MIMIC-III?
RapidMiner, an entrance to explore MIMIC-III?Sven Van Poucke, MD, PhD
 
Data Mining: Implementation of Data Mining Techniques using RapidMiner software
Data Mining: Implementation of Data Mining Techniques using RapidMiner softwareData Mining: Implementation of Data Mining Techniques using RapidMiner software
Data Mining: Implementation of Data Mining Techniques using RapidMiner softwareMohammed Kharma
 
Introduction to RapidMiner Studio V7
Introduction to RapidMiner Studio V7Introduction to RapidMiner Studio V7
Introduction to RapidMiner Studio V7geraldinegray
 
Data Mining Tools / Orange
Data Mining Tools / OrangeData Mining Tools / Orange
Data Mining Tools / OrangeYasemin Karaman
 
Data Mining and Big Data Challenges and Research Opportunities
Data Mining and Big Data Challenges and Research OpportunitiesData Mining and Big Data Challenges and Research Opportunities
Data Mining and Big Data Challenges and Research OpportunitiesKathirvel Ayyaswamy
 
RapidMiner: Introduction To Rapid Miner
RapidMiner: Introduction To Rapid MinerRapidMiner: Introduction To Rapid Miner
RapidMiner: Introduction To Rapid MinerRapidmining Content
 
DATA MINING TOOL- ORANGE
DATA MINING TOOL- ORANGEDATA MINING TOOL- ORANGE
DATA MINING TOOL- ORANGENeeraj Goswami
 
Data mining tools (R , WEKA, RAPID MINER, ORANGE)
Data mining tools (R , WEKA, RAPID MINER, ORANGE)Data mining tools (R , WEKA, RAPID MINER, ORANGE)
Data mining tools (R , WEKA, RAPID MINER, ORANGE)Krishna Petrochemicals
 
Data Mining and Business Intelligence Tools
Data Mining and Business Intelligence ToolsData Mining and Business Intelligence Tools
Data Mining and Business Intelligence ToolsMotaz Saad
 

Viewers also liked (17)

Data mining tools used in business intelligence
Data mining tools used in business intelligenceData mining tools used in business intelligence
Data mining tools used in business intelligence
 
Data mining tools
Data mining toolsData mining tools
Data mining tools
 
A comparative analysis of data mining tools for performance mapping of wlan data
A comparative analysis of data mining tools for performance mapping of wlan dataA comparative analysis of data mining tools for performance mapping of wlan data
A comparative analysis of data mining tools for performance mapping of wlan data
 
Slides PAPIs.io'14 RapidMiner
Slides PAPIs.io'14 RapidMinerSlides PAPIs.io'14 RapidMiner
Slides PAPIs.io'14 RapidMiner
 
Hadoop World 2011: Radoop: a Graphical Analytics Tool for Big Data - Gabor Ma...
Hadoop World 2011: Radoop: a Graphical Analytics Tool for Big Data - Gabor Ma...Hadoop World 2011: Radoop: a Graphical Analytics Tool for Big Data - Gabor Ma...
Hadoop World 2011: Radoop: a Graphical Analytics Tool for Big Data - Gabor Ma...
 
M Chambers and RapidMiner Overview for Babson class
M Chambers and RapidMiner Overview for Babson classM Chambers and RapidMiner Overview for Babson class
M Chambers and RapidMiner Overview for Babson class
 
RapidMiner, an entrance to explore MIMIC-III?
RapidMiner, an entrance to explore MIMIC-III?RapidMiner, an entrance to explore MIMIC-III?
RapidMiner, an entrance to explore MIMIC-III?
 
Data Mining: Implementation of Data Mining Techniques using RapidMiner software
Data Mining: Implementation of Data Mining Techniques using RapidMiner softwareData Mining: Implementation of Data Mining Techniques using RapidMiner software
Data Mining: Implementation of Data Mining Techniques using RapidMiner software
 
Rapidminer
RapidminerRapidminer
Rapidminer
 
Introduction to RapidMiner Studio V7
Introduction to RapidMiner Studio V7Introduction to RapidMiner Studio V7
Introduction to RapidMiner Studio V7
 
Data Mining Tools / Orange
Data Mining Tools / OrangeData Mining Tools / Orange
Data Mining Tools / Orange
 
Data Mining and Big Data Challenges and Research Opportunities
Data Mining and Big Data Challenges and Research OpportunitiesData Mining and Big Data Challenges and Research Opportunities
Data Mining and Big Data Challenges and Research Opportunities
 
RapidMiner: Introduction To Rapid Miner
RapidMiner: Introduction To Rapid MinerRapidMiner: Introduction To Rapid Miner
RapidMiner: Introduction To Rapid Miner
 
DATA MINING TOOL- ORANGE
DATA MINING TOOL- ORANGEDATA MINING TOOL- ORANGE
DATA MINING TOOL- ORANGE
 
Data mining tools (R , WEKA, RAPID MINER, ORANGE)
Data mining tools (R , WEKA, RAPID MINER, ORANGE)Data mining tools (R , WEKA, RAPID MINER, ORANGE)
Data mining tools (R , WEKA, RAPID MINER, ORANGE)
 
Data Mining and Business Intelligence Tools
Data Mining and Business Intelligence ToolsData Mining and Business Intelligence Tools
Data Mining and Business Intelligence Tools
 
Terminology Machine Learning
Terminology Machine LearningTerminology Machine Learning
Terminology Machine Learning
 

Similar to Data mining tools overall

Open source presentation to Cork County Council
Open source presentation to Cork County CouncilOpen source presentation to Cork County Council
Open source presentation to Cork County CouncilTim Willoughby
 
Data Science at Scale Using Apache Spark and Apache Hadoop
Data Science at Scale Using Apache Spark and Apache HadoopData Science at Scale Using Apache Spark and Apache Hadoop
Data Science at Scale Using Apache Spark and Apache HadoopCloudera, Inc.
 
Know thy logos
Know thy logosKnow thy logos
Know thy logosVishal V
 
Global Open Source Development 2011-2014 Review and 2015 Forecast
Global Open Source Development 2011-2014 Review and 2015 ForecastGlobal Open Source Development 2011-2014 Review and 2015 Forecast
Global Open Source Development 2011-2014 Review and 2015 ForecastSammy Fung
 
Intro to open source - 101 presentation
Intro to open source - 101 presentationIntro to open source - 101 presentation
Intro to open source - 101 presentationJavier Perez
 
Which postgres is_right_for_me_20130517
Which postgres is_right_for_me_20130517Which postgres is_right_for_me_20130517
Which postgres is_right_for_me_20130517EDB
 
Sinergija 12 WP8 is around the corner
Sinergija 12 WP8 is around the cornerSinergija 12 WP8 is around the corner
Sinergija 12 WP8 is around the cornerCatalin Gheorghiu
 
Top 10 DevOps tools for software development
Top 10 DevOps tools for software development Top 10 DevOps tools for software development
Top 10 DevOps tools for software development Mobiloitte
 
X tuple open erp system
X tuple open erp system X tuple open erp system
X tuple open erp system amiteshg
 
Pandas & Cloudera: Scaling the Python Data Experience
Pandas & Cloudera: Scaling the Python Data ExperiencePandas & Cloudera: Scaling the Python Data Experience
Pandas & Cloudera: Scaling the Python Data ExperienceTuri, Inc.
 
Ibis: Scaling the Python Data Experience
Ibis: Scaling the Python Data ExperienceIbis: Scaling the Python Data Experience
Ibis: Scaling the Python Data ExperienceWes McKinney
 
2014.07.11 biginsights data2014
2014.07.11 biginsights data20142014.07.11 biginsights data2014
2014.07.11 biginsights data2014Wilfried Hoge
 
Coding Secure Infrastructure in the Cloud using the PIE framework
Coding Secure Infrastructure in the Cloud using the PIE frameworkCoding Secure Infrastructure in the Cloud using the PIE framework
Coding Secure Infrastructure in the Cloud using the PIE frameworkJames Wickett
 
Big Data Technologies.pdf
Big Data Technologies.pdfBig Data Technologies.pdf
Big Data Technologies.pdfRAHULRAHU8
 
Android Workshop Part 1
Android Workshop Part 1Android Workshop Part 1
Android Workshop Part 1NAILBITER
 
UI Dev in Big data world using open source
UI Dev in Big data world using open sourceUI Dev in Big data world using open source
UI Dev in Big data world using open sourceTech Triveni
 

Similar to Data mining tools overall (20)

Open source presentation to Cork County Council
Open source presentation to Cork County CouncilOpen source presentation to Cork County Council
Open source presentation to Cork County Council
 
Data Science at Scale Using Apache Spark and Apache Hadoop
Data Science at Scale Using Apache Spark and Apache HadoopData Science at Scale Using Apache Spark and Apache Hadoop
Data Science at Scale Using Apache Spark and Apache Hadoop
 
Know thy logos
Know thy logosKnow thy logos
Know thy logos
 
Global Open Source Development 2011-2014 Review and 2015 Forecast
Global Open Source Development 2011-2014 Review and 2015 ForecastGlobal Open Source Development 2011-2014 Review and 2015 Forecast
Global Open Source Development 2011-2014 Review and 2015 Forecast
 
Intro to open source - 101 presentation
Intro to open source - 101 presentationIntro to open source - 101 presentation
Intro to open source - 101 presentation
 
Which postgres is_right_for_me_20130517
Which postgres is_right_for_me_20130517Which postgres is_right_for_me_20130517
Which postgres is_right_for_me_20130517
 
Sinergija 12 WP8 is around the corner
Sinergija 12 WP8 is around the cornerSinergija 12 WP8 is around the corner
Sinergija 12 WP8 is around the corner
 
caseywest
caseywestcaseywest
caseywest
 
caseywest
caseywestcaseywest
caseywest
 
Chap004
Chap004Chap004
Chap004
 
Top 10 DevOps tools for software development
Top 10 DevOps tools for software development Top 10 DevOps tools for software development
Top 10 DevOps tools for software development
 
X tuple open erp system
X tuple open erp system X tuple open erp system
X tuple open erp system
 
Pandas & Cloudera: Scaling the Python Data Experience
Pandas & Cloudera: Scaling the Python Data ExperiencePandas & Cloudera: Scaling the Python Data Experience
Pandas & Cloudera: Scaling the Python Data Experience
 
Ibis: Scaling the Python Data Experience
Ibis: Scaling the Python Data ExperienceIbis: Scaling the Python Data Experience
Ibis: Scaling the Python Data Experience
 
2014.07.11 biginsights data2014
2014.07.11 biginsights data20142014.07.11 biginsights data2014
2014.07.11 biginsights data2014
 
Coding Secure Infrastructure in the Cloud using the PIE framework
Coding Secure Infrastructure in the Cloud using the PIE frameworkCoding Secure Infrastructure in the Cloud using the PIE framework
Coding Secure Infrastructure in the Cloud using the PIE framework
 
SamSegalResume
SamSegalResumeSamSegalResume
SamSegalResume
 
Big Data Technologies.pdf
Big Data Technologies.pdfBig Data Technologies.pdf
Big Data Technologies.pdf
 
Android Workshop Part 1
Android Workshop Part 1Android Workshop Part 1
Android Workshop Part 1
 
UI Dev in Big data world using open source
UI Dev in Big data world using open sourceUI Dev in Big data world using open source
UI Dev in Big data world using open source
 

More from Mohamed Sharique Vellikan

Technology The Driving Force Behind Remarketing RepossessedSurrendered Vehicl...
Technology The Driving Force Behind Remarketing RepossessedSurrendered Vehicl...Technology The Driving Force Behind Remarketing RepossessedSurrendered Vehicl...
Technology The Driving Force Behind Remarketing RepossessedSurrendered Vehicl...Mohamed Sharique Vellikan
 

More from Mohamed Sharique Vellikan (11)

Technology The Driving Force Behind Remarketing RepossessedSurrendered Vehicl...
Technology The Driving Force Behind Remarketing RepossessedSurrendered Vehicl...Technology The Driving Force Behind Remarketing RepossessedSurrendered Vehicl...
Technology The Driving Force Behind Remarketing RepossessedSurrendered Vehicl...
 
Market segmentation
Market segmentationMarket segmentation
Market segmentation
 
Mohamed sharique(shipping agents)
Mohamed sharique(shipping agents)Mohamed sharique(shipping agents)
Mohamed sharique(shipping agents)
 
Mohamed sharique (buying and leasing)
Mohamed sharique (buying and leasing)Mohamed sharique (buying and leasing)
Mohamed sharique (buying and leasing)
 
Quality control methods
Quality control methodsQuality control methods
Quality control methods
 
HR audit
HR auditHR audit
HR audit
 
Hero motocorp
Hero motocorpHero motocorp
Hero motocorp
 
big data and cloud computing
big data and cloud computingbig data and cloud computing
big data and cloud computing
 
Canada vs India Hofstede
Canada vs India HofstedeCanada vs India Hofstede
Canada vs India Hofstede
 
Chola builders project
Chola builders projectChola builders project
Chola builders project
 
CSR - Aditya birla group
CSR - Aditya birla groupCSR - Aditya birla group
CSR - Aditya birla group
 

Recently uploaded

Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...Milind Agarwal
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingsocarem879
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max PrincetonReal-Time AI Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max PrincetonTimothy Spann
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Boston Institute of Analytics
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our WorldEduminds Learning
 
Cyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded dataCyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded dataTecnoIncentive
 
Networking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptxNetworking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptxHimangsuNath
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Boston Institute of Analytics
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTBoston Institute of Analytics
 
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...Amil Baba Dawood bangali
 
Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Seán Kennedy
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfblazblazml
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxMike Bennett
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 217djon017
 
convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfSubhamKumar3239
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesTimothy Spann
 
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Thomas Poetter
 
What To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxWhat To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxSimranPal17
 
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...Dr Arash Najmaei ( Phd., MBA, BSc)
 

Recently uploaded (20)

Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
Unveiling the Role of Social Media Suspect Investigators in Preventing Online...
 
Insurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis ProjectInsurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis Project
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processing
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max PrincetonReal-Time AI Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max Princeton
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our World
 
Cyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded dataCyber awareness ppt on the recorded data
Cyber awareness ppt on the recorded data
 
Networking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptxNetworking Case Study prepared by teacher.pptx
Networking Case Study prepared by teacher.pptx
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
 
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
 
Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptx
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2
 
convolutional neural network and its applications.pdf
convolutional neural network and its applications.pdfconvolutional neural network and its applications.pdf
convolutional neural network and its applications.pdf
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
 
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
Minimizing AI Hallucinations/Confabulations and the Path towards AGI with Exa...
 
What To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxWhat To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptx
 
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
 

Data mining tools overall

  • 2. • Open source • Data visualization and analysis • Novice and experts • Through Python scripting • Available for all popular platforms, including Windows, Mac OS X and variants of Linux. • Founded on 1996 • Orange is distributed free under the GPL. • M&D at the Bioinformatics Laboratory of the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. Product Details Company Details Python is a widely used general-purpose, high-level programming language. GNU General Public License is the most widely used free software license
  • 3. Features • Visual Programming • Visualization • Interaction and Data Analytics • Large Toolbox • Scripting Interface • Extendable • Documentation • Open Source • Platform Independence Success Stories • Astra-Zeneca, a pharmaceutical giant, which uses Orange in drug development and sponsors the development of several related parts of Orange • At Jožef Stefan Institute, the visual programming interface has been upgraded in Orange4WS to support service-oriented architectures
  • 5. • Latest R-language engine for statistical computing • Open source, R- Enterprise, R-Cloud(Paid version ) • Data visualization and analysis up to 16 TB • Extended capabilities with reproducible R tool Kits • Windows , Mac OS and variants of Linux. • Founded on 1993 in New Zealand • Robert and Rossa pioneer in R language development . • R has General Public Licence. • Many Big MNC companies are using R software. Product Details Company Details
  • 6. Useful Functions • Graphics Visualization • Spatial Data Analysis • Clustering • Text Mining • Social Network Analysis and Graph mining • Statistics • Data Manipulation Success Stories • Bank of America • Bing • Facebook • Ford • Google
  • 8. • Open source • a collection of machine learning algorithms • Data visualization and analysis • Java based platform • Most researchers and practitioners • Founded on 1997 • University of Waikato Product Details Company Details Public License is the most widely used free software license
  • 9. Features • General public license • GUI for interacting • Explorer is the main user interface of WEKA • primitive tasks including data pre-processing, classification, regression, clustering, association rules and visualization • Execute data files in multiple format • One exceptional feature of WEKA is the database connection using JDBC with any RDBMS package • The Weka mailing list has over 1100 subscribers in 50 countries, including subscribers from many major companies such as Rechtsportal Success Stories
  • 11. • Open source. • Data visualization and analysis • Machine Learning • Data Mining, Text Mining. • Business Intelligence. • Works on java runtime. • Available on all major operating systems and platforms • Started as YALE in 2001 by Ralf Klinkenberg, Ingo Mierswa, and Simon Fische • In 2006 it was renamed by Rapidminer since developed by Rapid-1 founded by Ralf Klinkenberg, Ingo Mierswa • Licensed by AGPL. Product Details Company Details
  • 12. Features • A visual - code-free - environment, so no programming needed • Design of analysis processes • Predictive analytics (with pre-made templates) • Data loading • Data transformation • Data Modelling • Data visualization (with lots of visualizations) • Allows you to work with different types and sizes of data sources • Platform Independence. • Acts as a powerful scripting language engine along with a graphical user • Modular operator concept. • CISCO • PAYPAL • EBAY • MIELE • VOLKSWAGEN Success Stories
  • 14. COMPARISON OF ALL TOOLS WEKA RAPIDMINER R- PROGRAMMING ORANGE FORMATS SUPPORTED ONLY 4 FILE FORMATS ARE SUPPORTED SUPPORTS MORE FILE FORMATS (Approx 22) SUPPORTS MORE FILE FORMATS SUPPORTS MORE FILE FORMATS USER INTERFACE EASY USER INTERFACE DIFFICULT USER INTERFACE SIMPLE IN UNIX OS,DIFFICULT IN WINDOWS AND MAC EASY CONNECTIVITY WORSE CONNECTIVITY WITH EXCEL AND NON JAVA DATABASES EASILY CONNECTED WITH EXCEL EASY CONNECTIVITY WITH EXCEL AND OTHER DATABASES BETTER THAN WEKA
  • 15. Orange has elegant and concise scripting and can also be run in an ETL GUI mode. R has elegant and concise scripting integrated with a vast statistical library. RapidMiner has a lot of functionality, is polished and has good connectivity. WEKA is the easiest GUI to learn and use.
  • 16. • http://old.biolab.si/ • http://en.wikipedia.org/ • http://www.predictiveanalyticsto day.com/ • http://thenewstack.io/ • www.facebook.com/ • www.slideshare.net/ • www.kdnuggets.com/ • www.researchgate.net • https://rapidminer.com/ • www.r-project.org • sourceforge.net/projects/weka • www.thearling.com

Editor's Notes

  1. contains a GUI for interacting with data files and producing visual results
  2. Explorer has several panels providing access to the main components of the workbench: the Preprocess panel has facilities for importing data from a database, a CSV file, etc, and to preprocess this data using a filtering algorithm. Such filters can be used to transform the data and make it possible to delete instances and attributes as per specific criteria. The Classify panel provides the features to apply classification and regression algorithms to the dataset, to estimate the accuracy of the resulting predictive model and visualise erroneous predictions, ROC curves or the model. The Associate panel provides the access for association rule learning to identify the interrelationships between attributes in the data. The Cluster panel or module provides access to the clustering techniques, including simple k-means algorithm and many others. The Select attributes panel provides access to the algorithms for the identification of the most predictive attributes in a dataset. The Visualize panel depicts a scatter plot matrix in which individual scatter plots can be selected, enlarged and analysed using various selection operators.