Commenced in January 2007
Paper Count: 30184
Weka Based Desktop Data Mining as Web Service
Abstract:Data mining is the process of sifting through large volumes of data, analyzing data from different perspectives and summarizing it into useful information. One of the widely used desktop applications for data mining is the Weka tool which is nothing but a collection of machine learning algorithms implemented in Java and open sourced under the General Public License (GPL). A web service is a software system designed to support interoperable machine to machine interaction over a network using SOAP messages. Unlike a desktop application, a web service is easy to upgrade, deliver and access and does not occupy any memory on the system. Keeping in mind the advantages of a web service over a desktop application, in this paper we are demonstrating how this Java based desktop data mining application can be implemented as a web service to support data mining across the internet.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332846Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3663
 Witten Ian H, and Frank Eibe, Data Mining Practical Machine Learning Tools and Techniques, Academic Press, pp 14-395.
 Frank Eibe,Hall, Trigg, Holmes, Data Mining in Bioinformatics using Weka, pp.1-2. Bioinformatics Volume:20, Issue:15, Pages: 2479-2481 ISSN: 1367-4803, ISBN 1460-2059.
 Bill.Palace, Technology note prepared for Management 274A, 1996. from http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/p alace/datamining.htm
 University of Waikato, Weka 3: Data Mining Software in Java. from http://www.cs.waikato.ac.nz/~ml/weka/http://www.cs.waikato.ac.nz/~ml /weka/
 Haridas Mandar, CIS764-Step By Step Tutorial for Weka,2008. http://www.docstoc.com/docs/2582601/CIS764---Step-By-Step- Tutorial-for-Weka-By-Mandar-Haridas.
 Markov Zdravko, and Russell Ingrid, An Introduction to the WEKA Data Mining System, 2006,Proceedings of the 11th annual SIGCSE conference on Innovation and technology in Computer Science Education, Bologna, Italy, 367-368.
 Dimov Rossen, WEKA: Practical Machine Learning Tools and Techniques in Java. from http://www.dfki.de/~kipp/seminar_ws0607/slides/Dimov_WEKA.pdf.
 Statsoft Electronic textbook on cluster analysis from: http://www.statsoft.com/textbook/cluster-analysis/
 Witten Ian H, and Frank Eibe, WEKA Machine Learning Algorithms in Java.
 Mark F.Hornick, Eric Marcade,Sunil Venkayala,Java Data Mining Strategy,Standard, and Practice,Morgan Kauffmann Series, pp 3-116.