SlideShare uma empresa Scribd logo
Ramin Vakili
&
Amin Afzali
DATA MINING APPLICATIONS
2
RAPID MINER
•What’s Rapid Miner?
•Advantages of Rapid
Miner:
•Special features in this
App:
•Open sours
•Wrote by Java
•Developed since 2001
•Try to support all of the data mining algorithms
•Ability of adding the other open sours data mining to App
•Give report and copy of the implementation of the algorithm
•Perfect GUI
•Match with most out puts of softwares like Excel
•Quick correcting and error detection
•Online video learning
•Help of many functions and suitable wiki in website of developer app
•Ability of running different algorithms in same time and comparing
together
•Compatible with different operation systems like Linux, Windows, Mac
•Text finding tool in App
•Add all of Weka learning algorithm after updating
3
.
•What’s Scipy?
•Advantages of Scipy:
•Disadvantages of
Scipy:
•SciPy (pronounced “Sigh Pie”) is a computing environment
and open source ecosystem of software for the Python
programming language that support some data mining
algorithms.
•For mathematic usage
•Data mining with Scipy is simple and complete because of Python!
•Learning Algorithms in Scipy is not complete yet
4
•What’s Weka?
•Advantages:
• Open source data mining App
• Contain many data mining libraries
• Wrote by Java language in the Waikato university
• Have many learning packages
• Suitable GUI
• Specially for data mining
• Simple workplace
• Suitable tool for text mining
• Ability of running several learning algorithms and
comparing
• Wrote by Java and can run in different platforms
5
&
Similarity Difference
Wrote by Java Poor performance in connecting to files
containing Excel data and data bases
aren’t Java base
Published under the GPL license Reading CSV files in Weka have not
suitable organize
Rapid miner upload many Weka
algorithms in own workplace
Rapid miner have better GUI than Weka
6
WHY WE CHOOSE RAPID MINER?
•Western Europe, with 37 percent of votes
•North America, with 35 percent of votes
•Eastern Europe, with 10 percent votes
•Asia, with 6 percent votes
•Oceania, with 4 percent votes
•Latin America, with 4 percent votes
•Africa and middle East, with 4 percent votes
0 5 10 15 20 25 30 35 40
Rapid Miner
R
Excel
SAS
Waka
27.7
23.3
21.8
13.6
11.8
37.8
29.8
24.3
12.1
14.4
Usage of data mining Apps in 2011 & 2010
2010
2011
7
RAPID MINER DATA MINING TOOL
•Analytical processing
design
•Performance and
flexibility
•Scalability
•Input data format
• More than 50000 download Since 2001
• Rapid-I for companies
• First place in IT challenges Europe named open source
business award
• More users that other in KDnuggets challenge for 4th
time in 2011
• Quik fixes
• Meta data transformation
• Breakpoint
• Rapid miner have more than 500 operator like:
• Data processing
• Modeling
• Text mining
• Web mining
• Opinion mining
• Rapid miner have 20 ways for visualize
• Is like Rational database
• On the fly
• Churn reduction
• Customer retention
• Data flows
8
• Compatibility with data bases like:
• Oracle
• IBM DB2
• Microsoft SQL server
• MySQL
• Excel
• Access
• SPSS
HOW TO INSTALL First go to
www.rapidminer.com
9

Mais conteúdo relacionado

Mais procurados

Translation Automation Going Cloud: The New Landscape for Professional Transl...
Translation Automation Going Cloud: The New Landscape for Professional Transl...Translation Automation Going Cloud: The New Landscape for Professional Transl...
Translation Automation Going Cloud: The New Landscape for Professional Transl...
ABBYY Language Serivces
 

Mais procurados (20)

Testing Mobile Applications With Telerik Platform
Testing Mobile Applications With Telerik PlatformTesting Mobile Applications With Telerik Platform
Testing Mobile Applications With Telerik Platform
 
Maintainable Machine Learning Products
Maintainable Machine Learning ProductsMaintainable Machine Learning Products
Maintainable Machine Learning Products
 
ReliefWeb's Journey from RSS Feed to Public API
ReliefWeb's Journey from RSS Feed to Public APIReliefWeb's Journey from RSS Feed to Public API
ReliefWeb's Journey from RSS Feed to Public API
 
Using RAML 1.0 Like a Pro
Using RAML 1.0 Like a ProUsing RAML 1.0 Like a Pro
Using RAML 1.0 Like a Pro
 
Five Ways to Fix Your SQL Server Dev-Test Problems
Five Ways to Fix Your SQL Server Dev-Test Problems Five Ways to Fix Your SQL Server Dev-Test Problems
Five Ways to Fix Your SQL Server Dev-Test Problems
 
Your API Strategy: Why Boring is Best
Your API Strategy: Why Boring is BestYour API Strategy: Why Boring is Best
Your API Strategy: Why Boring is Best
 
Nordstrom Customer Presentation
Nordstrom Customer PresentationNordstrom Customer Presentation
Nordstrom Customer Presentation
 
GraphQL.net
GraphQL.netGraphQL.net
GraphQL.net
 
Building and Delivering Reports from your Web and Mobile Apps with Telerik Re...
Building and Delivering Reports from your Web and Mobile Apps with Telerik Re...Building and Delivering Reports from your Web and Mobile Apps with Telerik Re...
Building and Delivering Reports from your Web and Mobile Apps with Telerik Re...
 
Apica Company Summary 2016
Apica Company Summary 2016Apica Company Summary 2016
Apica Company Summary 2016
 
Sri monthly presentation 2015
Sri monthly presentation 2015Sri monthly presentation 2015
Sri monthly presentation 2015
 
Sri monthly presentation 2016
Sri monthly presentation 2016Sri monthly presentation 2016
Sri monthly presentation 2016
 
A look ahead at RAP (ESE 2010)
A look ahead at RAP (ESE 2010)A look ahead at RAP (ESE 2010)
A look ahead at RAP (ESE 2010)
 
New Enterprisre Capabilities in Telerik Platform
New Enterprisre Capabilities in Telerik PlatformNew Enterprisre Capabilities in Telerik Platform
New Enterprisre Capabilities in Telerik Platform
 
Introduction to Kitura - Swift Hong Kong Meetup 2016 July
Introduction to Kitura - Swift Hong Kong Meetup 2016 JulyIntroduction to Kitura - Swift Hong Kong Meetup 2016 July
Introduction to Kitura - Swift Hong Kong Meetup 2016 July
 
Preparing Big Data for Analysis with Easyl
Preparing Big Data for Analysis with EasylPreparing Big Data for Analysis with Easyl
Preparing Big Data for Analysis with Easyl
 
Translation Automation Going Cloud: The New Landscape for Professional Transl...
Translation Automation Going Cloud: The New Landscape for Professional Transl...Translation Automation Going Cloud: The New Landscape for Professional Transl...
Translation Automation Going Cloud: The New Landscape for Professional Transl...
 
AWS User Group - Survey Results and Building APIs on AWS
AWS User Group - Survey Results and Building APIs on AWSAWS User Group - Survey Results and Building APIs on AWS
AWS User Group - Survey Results and Building APIs on AWS
 
JIRA 5 collaborative platform
JIRA 5 collaborative platformJIRA 5 collaborative platform
JIRA 5 collaborative platform
 
The Atlassian Tool Suite for Collaborative Science
The Atlassian Tool Suite for Collaborative ScienceThe Atlassian Tool Suite for Collaborative Science
The Atlassian Tool Suite for Collaborative Science
 

Semelhante a _rapid_miner

Five Early Challenges Of Building Streaming Fast Data Applications
Five Early Challenges Of Building Streaming Fast Data ApplicationsFive Early Challenges Of Building Streaming Fast Data Applications
Five Early Challenges Of Building Streaming Fast Data Applications
Lightbend
 

Semelhante a _rapid_miner (20)

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 tools overall
Data mining tools overallData mining tools overall
Data mining tools overall
 
Open Source & Identity Management
Open Source & Identity ManagementOpen Source & Identity Management
Open Source & Identity Management
 
Lantea platform
Lantea platformLantea platform
Lantea platform
 
Application Insights for Integration Developers
Application Insights for Integration DevelopersApplication Insights for Integration Developers
Application Insights for Integration Developers
 
Analytical tools
Analytical toolsAnalytical tools
Analytical tools
 
Rootconf 2017 - State of the Open Source monitoring landscape
Rootconf 2017 - State of the Open Source monitoring landscape Rootconf 2017 - State of the Open Source monitoring landscape
Rootconf 2017 - State of the Open Source monitoring landscape
 
API ARU-ARU
API ARU-ARUAPI ARU-ARU
API ARU-ARU
 
SharePoint Apps 101
SharePoint Apps 101SharePoint Apps 101
SharePoint Apps 101
 
SharePoint Development
SharePoint DevelopmentSharePoint Development
SharePoint Development
 
BrainQuest-DevOps
BrainQuest-DevOpsBrainQuest-DevOps
BrainQuest-DevOps
 
Kontent.ai DevMeetup #1 - Evoluce prvního veřejného API v hotelovém světě
Kontent.ai DevMeetup #1 - Evoluce prvního veřejného API v hotelovém světěKontent.ai DevMeetup #1 - Evoluce prvního veřejného API v hotelovém světě
Kontent.ai DevMeetup #1 - Evoluce prvního veřejného API v hotelovém světě
 
APEX Alpe Adria Mike Hichwa Keynote April 11th 2019- Zagreb
APEX Alpe Adria Mike Hichwa Keynote April 11th 2019- ZagrebAPEX Alpe Adria Mike Hichwa Keynote April 11th 2019- Zagreb
APEX Alpe Adria Mike Hichwa Keynote April 11th 2019- Zagreb
 
AD1545 - Extending the XPages Extension Library
AD1545 - Extending the XPages Extension LibraryAD1545 - Extending the XPages Extension Library
AD1545 - Extending the XPages Extension Library
 
Five Early Challenges Of Building Streaming Fast Data Applications
Five Early Challenges Of Building Streaming Fast Data ApplicationsFive Early Challenges Of Building Streaming Fast Data Applications
Five Early Challenges Of Building Streaming Fast Data Applications
 
Fast, Flexible Application Development with Oracle Database Cloud Service
Fast, Flexible Application Development with Oracle Database Cloud ServiceFast, Flexible Application Development with Oracle Database Cloud Service
Fast, Flexible Application Development with Oracle Database Cloud Service
 
Gradual Migration to MicroProfile
Gradual Migration to MicroProfileGradual Migration to MicroProfile
Gradual Migration to MicroProfile
 
Gradual migration to MicroProfile
Gradual migration to MicroProfileGradual migration to MicroProfile
Gradual migration to MicroProfile
 
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
 
2012 RightScale Conference NYC - Jeff Gelb, Director of Technology Strategy, ...
2012 RightScale Conference NYC - Jeff Gelb, Director of Technology Strategy, ...2012 RightScale Conference NYC - Jeff Gelb, Director of Technology Strategy, ...
2012 RightScale Conference NYC - Jeff Gelb, Director of Technology Strategy, ...
 

Último

Laundry management system project report.pdf
Laundry management system project report.pdfLaundry management system project report.pdf
Laundry management system project report.pdf
Kamal Acharya
 
Digital Signal Processing Lecture notes n.pdf
Digital Signal Processing Lecture notes n.pdfDigital Signal Processing Lecture notes n.pdf
Digital Signal Processing Lecture notes n.pdf
AbrahamGadissa
 
School management system project report.pdf
School management system project report.pdfSchool management system project report.pdf
School management system project report.pdf
Kamal Acharya
 

Último (20)

Introduction to Machine Learning Unit-4 Notes for II-II Mechanical Engineering
Introduction to Machine Learning Unit-4 Notes for II-II Mechanical EngineeringIntroduction to Machine Learning Unit-4 Notes for II-II Mechanical Engineering
Introduction to Machine Learning Unit-4 Notes for II-II Mechanical Engineering
 
Soil Testing Instruments by aimil ltd.- California Bearing Ratio apparatus, c...
Soil Testing Instruments by aimil ltd.- California Bearing Ratio apparatus, c...Soil Testing Instruments by aimil ltd.- California Bearing Ratio apparatus, c...
Soil Testing Instruments by aimil ltd.- California Bearing Ratio apparatus, c...
 
Event Management System Vb Net Project Report.pdf
Event Management System Vb Net  Project Report.pdfEvent Management System Vb Net  Project Report.pdf
Event Management System Vb Net Project Report.pdf
 
The battle for RAG, explore the pros and cons of using KnowledgeGraphs and Ve...
The battle for RAG, explore the pros and cons of using KnowledgeGraphs and Ve...The battle for RAG, explore the pros and cons of using KnowledgeGraphs and Ve...
The battle for RAG, explore the pros and cons of using KnowledgeGraphs and Ve...
 
Dairy management system project report..pdf
Dairy management system project report..pdfDairy management system project report..pdf
Dairy management system project report..pdf
 
Democratizing Fuzzing at Scale by Abhishek Arya
Democratizing Fuzzing at Scale by Abhishek AryaDemocratizing Fuzzing at Scale by Abhishek Arya
Democratizing Fuzzing at Scale by Abhishek Arya
 
Pharmacy management system project report..pdf
Pharmacy management system project report..pdfPharmacy management system project report..pdf
Pharmacy management system project report..pdf
 
The Benefits and Techniques of Trenchless Pipe Repair.pdf
The Benefits and Techniques of Trenchless Pipe Repair.pdfThe Benefits and Techniques of Trenchless Pipe Repair.pdf
The Benefits and Techniques of Trenchless Pipe Repair.pdf
 
Laundry management system project report.pdf
Laundry management system project report.pdfLaundry management system project report.pdf
Laundry management system project report.pdf
 
Cloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptx
Cloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptxCloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptx
Cloud-Computing_CSE311_Computer-Networking CSE GUB BD - Shahidul.pptx
 
Supermarket billing system project report..pdf
Supermarket billing system project report..pdfSupermarket billing system project report..pdf
Supermarket billing system project report..pdf
 
KIT-601 Lecture Notes-UNIT-3.pdf Mining Data Stream
KIT-601 Lecture Notes-UNIT-3.pdf Mining Data StreamKIT-601 Lecture Notes-UNIT-3.pdf Mining Data Stream
KIT-601 Lecture Notes-UNIT-3.pdf Mining Data Stream
 
Introduction to Machine Learning Unit-5 Notes for II-II Mechanical Engineering
Introduction to Machine Learning Unit-5 Notes for II-II Mechanical EngineeringIntroduction to Machine Learning Unit-5 Notes for II-II Mechanical Engineering
Introduction to Machine Learning Unit-5 Notes for II-II Mechanical Engineering
 
A CASE STUDY ON ONLINE TICKET BOOKING SYSTEM PROJECT.pdf
A CASE STUDY ON ONLINE TICKET BOOKING SYSTEM PROJECT.pdfA CASE STUDY ON ONLINE TICKET BOOKING SYSTEM PROJECT.pdf
A CASE STUDY ON ONLINE TICKET BOOKING SYSTEM PROJECT.pdf
 
RESORT MANAGEMENT AND RESERVATION SYSTEM PROJECT REPORT.pdf
RESORT MANAGEMENT AND RESERVATION SYSTEM PROJECT REPORT.pdfRESORT MANAGEMENT AND RESERVATION SYSTEM PROJECT REPORT.pdf
RESORT MANAGEMENT AND RESERVATION SYSTEM PROJECT REPORT.pdf
 
KIT-601 Lecture Notes-UNIT-4.pdf Frequent Itemsets and Clustering
KIT-601 Lecture Notes-UNIT-4.pdf Frequent Itemsets and ClusteringKIT-601 Lecture Notes-UNIT-4.pdf Frequent Itemsets and Clustering
KIT-601 Lecture Notes-UNIT-4.pdf Frequent Itemsets and Clustering
 
Construction method of steel structure space frame .pptx
Construction method of steel structure space frame .pptxConstruction method of steel structure space frame .pptx
Construction method of steel structure space frame .pptx
 
Digital Signal Processing Lecture notes n.pdf
Digital Signal Processing Lecture notes n.pdfDigital Signal Processing Lecture notes n.pdf
Digital Signal Processing Lecture notes n.pdf
 
School management system project report.pdf
School management system project report.pdfSchool management system project report.pdf
School management system project report.pdf
 
BRAKING SYSTEM IN INDIAN RAILWAY AutoCAD DRAWING
BRAKING SYSTEM IN INDIAN RAILWAY AutoCAD DRAWINGBRAKING SYSTEM IN INDIAN RAILWAY AutoCAD DRAWING
BRAKING SYSTEM IN INDIAN RAILWAY AutoCAD DRAWING
 

_rapid_miner

  • 3. RAPID MINER •What’s Rapid Miner? •Advantages of Rapid Miner: •Special features in this App: •Open sours •Wrote by Java •Developed since 2001 •Try to support all of the data mining algorithms •Ability of adding the other open sours data mining to App •Give report and copy of the implementation of the algorithm •Perfect GUI •Match with most out puts of softwares like Excel •Quick correcting and error detection •Online video learning •Help of many functions and suitable wiki in website of developer app •Ability of running different algorithms in same time and comparing together •Compatible with different operation systems like Linux, Windows, Mac •Text finding tool in App •Add all of Weka learning algorithm after updating 3
  • 4. . •What’s Scipy? •Advantages of Scipy: •Disadvantages of Scipy: •SciPy (pronounced “Sigh Pie”) is a computing environment and open source ecosystem of software for the Python programming language that support some data mining algorithms. •For mathematic usage •Data mining with Scipy is simple and complete because of Python! •Learning Algorithms in Scipy is not complete yet 4
  • 5. •What’s Weka? •Advantages: • Open source data mining App • Contain many data mining libraries • Wrote by Java language in the Waikato university • Have many learning packages • Suitable GUI • Specially for data mining • Simple workplace • Suitable tool for text mining • Ability of running several learning algorithms and comparing • Wrote by Java and can run in different platforms 5
  • 6. & Similarity Difference Wrote by Java Poor performance in connecting to files containing Excel data and data bases aren’t Java base Published under the GPL license Reading CSV files in Weka have not suitable organize Rapid miner upload many Weka algorithms in own workplace Rapid miner have better GUI than Weka 6
  • 7. WHY WE CHOOSE RAPID MINER? •Western Europe, with 37 percent of votes •North America, with 35 percent of votes •Eastern Europe, with 10 percent votes •Asia, with 6 percent votes •Oceania, with 4 percent votes •Latin America, with 4 percent votes •Africa and middle East, with 4 percent votes 0 5 10 15 20 25 30 35 40 Rapid Miner R Excel SAS Waka 27.7 23.3 21.8 13.6 11.8 37.8 29.8 24.3 12.1 14.4 Usage of data mining Apps in 2011 & 2010 2010 2011 7
  • 8. RAPID MINER DATA MINING TOOL •Analytical processing design •Performance and flexibility •Scalability •Input data format • More than 50000 download Since 2001 • Rapid-I for companies • First place in IT challenges Europe named open source business award • More users that other in KDnuggets challenge for 4th time in 2011 • Quik fixes • Meta data transformation • Breakpoint • Rapid miner have more than 500 operator like: • Data processing • Modeling • Text mining • Web mining • Opinion mining • Rapid miner have 20 ways for visualize • Is like Rational database • On the fly • Churn reduction • Customer retention • Data flows 8 • Compatibility with data bases like: • Oracle • IBM DB2 • Microsoft SQL server • MySQL • Excel • Access • SPSS
  • 9. HOW TO INSTALL First go to www.rapidminer.com 9