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| {{Data mining and learning analytics tools | | {{Data mining and learning analytics tools |
| |field_screenshot= | | |field_screenshot=Weka.png |
| |field_name=Weka | | |field_name=Weka |
| |field_developers=Machine Learning Group at the University of Waikato | | |field_developers=Machine Learning Group at the University of Waikato |
Revision as of 17:48, 26 February 2014
Weka 3.6 (2013/07/30)
Developed by: Machine Learning Group at the University of Waikato
License: GPL / GNU General Public License
Web page : Tool homepage
Tool type :
The last edition of this page was on: 2014/02/26
The Completion level of this page is : Low
The last edition of this page was on: 2014/02/26 The Completion level of this page is : Low
SHORT DESCRIPTION
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
TOOL CHARACTERISTICS
Usability
Authors of this page consider that this tool is '.
Tool orientation
This tool is designed for general purpose analysis.
Data mining type
This tool is made for '.
Manipulation type
This tool is designed for '.
Tool objective(s) in the field of Learning Sciences |
☑ Analysis & Visualisation of data
☑ Predicting student performance
☑ Student modelling
☑ Social Network Analysis (SNA)
☑ Constructing courseware
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☑ Providing feedback for supporting instructors:
☑ Recommendations for students
☑ Grouping students:
☑ Developing concept maps:
☑ Planning/scheduling/monitoring
☑ Experimentation/observation
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ABOUT USERS
Tool is suitable for:
Students/Learners/Consumers
Teachers/Tutors/Managers
Researchers
Developers/Designers
Organisations/Institutions/Firms
Others
FREE TEXT
Tool version : Weka 3.6 2013/07/30 (blank line)
Developed by : Machine Learning Group at the University of Waikato (blank line)
Tool Web page : http://www.cs.waikato.ac.nz/ml/index.html (blank line)
Tool type : (blank line)
License:GPL / GNU General Public License
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SHORT DESCRIPTION
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
TOOL CHARACTERISTICS
Tool orientation |
Data mining type |
Usability |
This tool is designed for general purpose analysis. |
This tool is designed for . |
Authors of this page consider that this tool is . |
Data import format |
Data export format |
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. |
Tool objective(s) in the field of Learning Sciences |
☑ Analysis & Visualisation of data
☑ Predicting student performance
☑ Student modelling
☑ Social Network Analysis (SNA)
☑ Constructing courseware
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☑ Providing feedback for supporting instructors:
☑ Recommendations for students
☑ Grouping students:
☑ Developing concept maps:
☑ Planning/scheduling/monitoring
☑ Experimentation/observation
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Can perform data extraction of type:
Can perform data transformation of type:
Can perform data analysis of type:
Can perform data visualisation of type:
(These visualisations can be interactive and updated in "real time")
ABOUT USER
Tool is suitable for: |
Students/Learners/Consumers:☑ |
Teachers/Tutors/Managers:☑ |
Researchers:☑ |
Organisations/Institutions/Firms:☑ |
Others:☑ |
Required skills: |
Statistics: |
Programming: |
System administration: |
Data mining models: |
OTHER TOOL INFORMATION
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Weka.png
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Weka
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GPL / GNU General Public License
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Free&Open source
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Machine Learning Group at the University of Waikato
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2013/07/30
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3.6
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http://www.cs.waikato.ac.nz/ml/index.html
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Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
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General analysis
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Low
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