Maarof, Mohd. Aizaini and Kenari, Abdolreza Rasouli and Md. Sap, M. N. and Shamsi, Mahboubeh (2009) An intelligent weighted kernel K-means algorithm for high dimension data. In: 2nd International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2009. Article number 5273893 . Institute of Electrical and Electronics Engineers, New York, pp. 829-831. ISBN 978-142444457-1
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Official URL: http://dx.doi.org/10.1109/ICADIWT.2009.5273893
Clustering is a kind of unsupervised classification of objects into groups so that objects from the same cluster are more similar to each other than objects from different clusters. In this paper, we focus on Weighted Kernel K-Means method for its capability to handle nonlinear separability, noise, outliers and high dimensionality in the data. A new WKM algorithm has been proposed and tested on real Rice data. The results exposed by algorithm encourage the use of WKM for the solution of real world problems.
|Item Type:||Book Section|
|Additional Information:||2nd International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2009; London; 4 August 2009 through 6 August 2009|
|Uncontrolled Keywords:||classification accuracy, clustering, data mining, F-measure, Weighted kernel K-means, WKM algorithm|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System|
|Deposited By:||Ms Zalinda Shuratman|
|Deposited On:||12 Jul 2011 01:41|
|Last Modified:||12 Jul 2011 01:41|
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