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An intelligent weighted kernel K-means algorithm for high dimension data

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,...

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Main Authors: Maarof, Mohd. Aizaini, Kenari, Abdolreza Rasouli, Md. Sap, M. N., Shamsi, Mahboubeh
Format: Book Section
Published: Institute of Electrical and Electronics Engineers 2009
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/12985/
http://dx.doi.org/10.1109/ICADIWT.2009.5273893
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id my.utm.12985
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spelling my.utm.129852011-07-12T01:41:55Z http://eprints.utm.my/id/eprint/12985/ An intelligent weighted kernel K-means algorithm for high dimension data Maarof, Mohd. Aizaini Kenari, Abdolreza Rasouli Md. Sap, M. N. Shamsi, Mahboubeh QA75 Electronic computers. Computer science 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. Institute of Electrical and Electronics Engineers 2009 Book Section PeerReviewed 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 http://dx.doi.org/10.1109/ICADIWT.2009.5273893 DOI: 10.1109/ICADIWT.2009.5273893
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Maarof, Mohd. Aizaini
Kenari, Abdolreza Rasouli
Md. Sap, M. N.
Shamsi, Mahboubeh
An intelligent weighted kernel K-means algorithm for high dimension data
description 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.
format Book Section
author Maarof, Mohd. Aizaini
Kenari, Abdolreza Rasouli
Md. Sap, M. N.
Shamsi, Mahboubeh
author_facet Maarof, Mohd. Aizaini
Kenari, Abdolreza Rasouli
Md. Sap, M. N.
Shamsi, Mahboubeh
author_sort Maarof, Mohd. Aizaini
title An intelligent weighted kernel K-means algorithm for high dimension data
title_short An intelligent weighted kernel K-means algorithm for high dimension data
title_full An intelligent weighted kernel K-means algorithm for high dimension data
title_fullStr An intelligent weighted kernel K-means algorithm for high dimension data
title_full_unstemmed An intelligent weighted kernel K-means algorithm for high dimension data
title_sort intelligent weighted kernel k-means algorithm for high dimension data
publisher Institute of Electrical and Electronics Engineers
publishDate 2009
url http://eprints.utm.my/id/eprint/12985/
http://dx.doi.org/10.1109/ICADIWT.2009.5273893
_version_ 1643646086100811776
score 13.211869

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