{"appState":{"pageLoadApiCallsStatus":true},"articleState":{"article":{"headers":{"creationTime":"2016-10-04T20:30:00+00:00","modifiedTime":"2016-10-04T20:30:00+00:00","timestamp":"2022-09-14T18:16:12+00:00"},"data":{"breadcrumbs":[{"name":"Technology","_links":{"self":"https://dummies-api.dummies.com/v2/categories/33512"},"slug":"technology","categoryId":33512},{"name":"Information Technology","_links":{"self":"https://dummies-api.dummies.com/v2/categories/33572"},"slug":"information-technology","categoryId":33572},{"name":"AI","_links":{"self":"https://dummies-api.dummies.com/v2/categories/33574"},"slug":"ai","categoryId":33574},{"name":"Machine Learning","_links":{"self":"https://dummies-api.dummies.com/v2/categories/33575"},"slug":"machine-learning","categoryId":33575}],"title":"Machine Learning in Academia with Weka","strippedTitle":"machine learning in academia with weka","slug":"machine-learning-academia-weka","canonicalUrl":"","seo":{"metaDescription":"Weka (also available at Sourceforge.net ) is a collection of machine learning algorithms written in Java and developed at the University of Waikato, New Zealand","noIndex":0,"noFollow":0},"content":"<a href=\"http://www.cs.waikato.ac.nz/ml/weka/\">Weka</a> (also available at <a href=\"https://sourceforge.net/projects/weka/\">Sourceforge.net</a>) is a collection of machine learning algorithms written in Java and developed at the University of Waikato, New Zealand. The main purpose of Weka is to perform data-mining tasks, and initially, schools used it as a learning tool. Now the tool is included as part of the Pentaho business intelligence suite where Weka is used for business intelligence. You can use it for\r\n<ul>\r\n\t<li>Association rules</li>\r\n\t<li>Attribute selection</li>\r\n\t<li>Clustering</li>\r\n\t<li>Data preprocessing</li>\r\n\t<li>Data classification</li>\r\n\t<li>Data visualization</li>\r\n\t<li>Regression analysis</li>\r\n\t<li>Workflow analysis</li>\r\n</ul>\r\n<p class=\"article-tips remember\">The reasons that Weka works especially well in schools is that the Java code runs on nearly any platform and you can download Weka free. You can apply Weka algorithms directly to a dataset or use Weka within your own Java code, making the environment extremely flexible. The one downside of Weka is that it tends not to work well on really large datasets.</p>\r\nTo use Weka, you must also <a href=\"http://www.cs.waikato.ac.nz/ml/weka/requirements.html\">install an appropriate version of Java</a> on your system. You can use Weka with any DBMS that Java or a third-party Java add-on product supports through Java Database Connectivity (JDBC), so you have a wide selection of data sources from which to choose.","description":"<a href=\"http://www.cs.waikato.ac.nz/ml/weka/\">Weka</a> (also available at <a href=\"https://sourceforge.net/projects/weka/\">Sourceforge.net</a>) is a collection of machine learning algorithms written in Java and developed at the University of Waikato, New Zealand. The main purpose of Weka is to perform data-mining tasks, and initially, schools used it as a learning tool. Now the tool is included as part of the Pentaho business intelligence suite where Weka is used for business intelligence. You can use it for\r\n<ul>\r\n\t<li>Association rules</li>\r\n\t<li>Attribute selection</li>\r\n\t<li>Clustering</li>\r\n\t<li>Data preprocessing</li>\r\n\t<li>Data classification</li>\r\n\t<li>Data visualization</li>\r\n\t<li>Regression analysis</li>\r\n\t<li>Workflow analysis</li>\r\n</ul>\r\n<p class=\"article-tips remember\">The reasons that Weka works especially well in schools is that the Java code runs on nearly any platform and you can download Weka free. You can apply Weka algorithms directly to a dataset or use Weka within your own Java code, making the environment extremely flexible. The one downside of Weka is that it tends not to work well on really large datasets.</p>\r\nTo use Weka, you must also <a href=\"http://www.cs.waikato.ac.nz/ml/weka/requirements.html\">install an appropriate version of Java</a> on your system. 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Machine Learning in Academia with Weka

By: John Paul Mueller and Luca Massaron and

Updated: 10-04-2016

From The Book: Machine Learning For Dummies

Machine Learning For Dummies

Book image

Explore Book Buy On Amazon

Weka (also available at Sourceforge.net) is a collection of machine learning algorithms written in Java and developed at the University of Waikato, New Zealand. The main purpose of Weka is to perform data-mining tasks, and initially, schools used it as a learning tool. Now the tool is included as part of the Pentaho business intelligence suite where Weka is used for business intelligence. You can use it for

Association rules
Attribute selection
Clustering
Data preprocessing
Data classification
Data visualization
Regression analysis
Workflow analysis

The reasons that Weka works especially well in schools is that the Java code runs on nearly any platform and you can download Weka free. You can apply Weka algorithms directly to a dataset or use Weka within your own Java code, making the environment extremely flexible. The one downside of Weka is that it tends not to work well on really large datasets.

To use Weka, you must also install an appropriate version of Java on your system. You can use Weka with any DBMS that Java or a third-party Java add-on product supports through Java Database Connectivity (JDBC), so you have a wide selection of data sources from which to choose.

About This Article

This article is from the book:

Machine Learning For Dummies ,

About the book authors:

John Mueller has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). Luca Massaron, a Google Developer Expert (GDE),??interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques.

John Mueller has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). Luca Massaron, a Google Developer Expert (GDE),??interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques.

This article can be found in the category:

Machine Learning ,