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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| Format: | Book Section |
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Institute of Electrical and Electronics Engineers
2009
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| _version_ | 1796855169966669824 |
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| 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 |
| collection | ePrints |
| 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.
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| first_indexed | 2024-03-05T18:24:42Z |
| format | Book Section |
| id | utm.eprints-12985 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:24:42Z |
| publishDate | 2009 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | utm.eprints-129852011-07-12T01:41:55Z http://eprints.utm.my/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 |
| 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 |
| title | 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_short | An intelligent weighted kernel K-means algorithm for high dimension data |
| title_sort | intelligent weighted kernel k means algorithm for high dimension data |
| topic | QA75 Electronic computers. Computer science |
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