Rough Cluster Algorithm Based on Kernel Function View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

Tao Zhou , Yanning Zhang , Huiling Lu , Fang’an Deng , Fengxiao Wang

ABSTRACT

By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis. Through using Mercer kernel functions, samples in the original space were mapped into a high-dimensional feature space, which the difference among these samples in sample space was strengthened through kernel mapping, combining rough set with k-means to cluster in feature space. These samples were assigned into up-approximation or low-approximation of corresponding clustering centers, and then these data that were in up-approximation and low-approximation were combined and to update cluster center. Through this method, clustering precision was improved, clustering convergence speed was fast compared with classical clustering algorithms The results of simulation experiments show the feasibility and effectiveness of the kernel clustering algorithm. More... »

PAGES

172-179

References to SciGraph publications

Book

TITLE

Rough Sets and Knowledge Technology

ISBN

978-3-540-79720-3
978-3-540-79721-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-79721-0_27

DOI

http://dx.doi.org/10.1007/978-3-540-79721-0_27

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050624816


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154 https://www.grid.ac/institutes/grid.440588.5 schema:alternateName Northwestern Polytechnical University
155 schema:name School of Computer Science, Northwestern Polytechnical Univ., 710072, Xi’an, China
156 rdf:type schema:Organization
 




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