Rough kernel clustering algorithm with adaptive parameters

Authors:
Tao Zhou;Huiling Lu;Deren Yang;Jingxian Ma;Shouheng Tuo
Affiliations:
School of Science, Ningxia Medical university, Ningxia Yinchuan and Dept. Of Maths, Shaanxi University of Technology, Shaanxi Hanzhong;School of Science, Ningxia Medical university, Ningxia Yinchuan;School of Science, Ningxia Medical university, Ningxia Yinchuan;School of Science, Ningxia Medical university, Ningxia Yinchuan;Dept. Of Computer, Shaanxi University of Technology, Shaanxi Hanzhong
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

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Abstract

Through analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, Rough kernel k-means clustering algorithm with adaptive parameters, is proposed for clustering analysis in this paper. By using Mercer kernel functions, we can map the data in the original space to a highdimensional feature space, in which we can use rough k-means with adaptive parameters to perform clustering in feature space. Efficiently.The results of simulation experiments show the feasibility and effectiveness of the kernel clustering algorithm.