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.
[1] Witten Ian H, and Frank Eibe, Data Mining Practical Machine Learning
Tools and Techniques, Academic Press, pp 14-395.
[2] 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.
[3] Bill.Palace, Technology note prepared for Management 274A, 1996.
from
http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/p
alace/datamining.htm
[4] 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/
[5] 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.
[6] 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.
[7] http://en.wikipedia.org/wiki/WebServices/
[8] Dimov Rossen, WEKA: Practical Machine Learning Tools and
Techniques in Java. from
http://www.dfki.de/~kipp/seminar_ws0607/slides/Dimov_WEKA.pdf.
[9] Statsoft Electronic textbook on cluster analysis from:
http://www.statsoft.com/textbook/cluster-analysis/
[10] Witten Ian H, and Frank Eibe, WEKA Machine Learning Algorithms in
Java.
[11] Mark F.Hornick, Eric Marcade,Sunil Venkayala,Java Data Mining
Strategy,Standard, and Practice,Morgan Kauffmann Series, pp 3-116.
[12] http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
[1] Witten Ian H, and Frank Eibe, Data Mining Practical Machine Learning
Tools and Techniques, Academic Press, pp 14-395.
[2] 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.
[3] Bill.Palace, Technology note prepared for Management 274A, 1996.
from
http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/p
alace/datamining.htm
[4] 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/
[5] 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.
[6] 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.
[7] http://en.wikipedia.org/wiki/WebServices/
[8] Dimov Rossen, WEKA: Practical Machine Learning Tools and
Techniques in Java. from
http://www.dfki.de/~kipp/seminar_ws0607/slides/Dimov_WEKA.pdf.
[9] Statsoft Electronic textbook on cluster analysis from:
http://www.statsoft.com/textbook/cluster-analysis/
[10] Witten Ian H, and Frank Eibe, WEKA Machine Learning Algorithms in
Java.
[11] Mark F.Hornick, Eric Marcade,Sunil Venkayala,Java Data Mining
Strategy,Standard, and Practice,Morgan Kauffmann Series, pp 3-116.
[12] http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
@article{"International Journal of Information, Control and Computer Sciences:55999", author = "Sujala.D.Shetty and S.Vadivel and Sakshi Vaghella", title = "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.", keywords = "desktop application, Weka mining, web service", volume = "4", number = "4", pages = "709-19", }