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AN UNSUPERVISED KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM WITH KERNEL NORMALISATION.

Authors :
ZHOU, SHANG-MING
GAN, JOHN Q.
Source :
International Journal of Computational Intelligence & Applications. Dec2004, Vol. 4 Issue 4, p355-373. 19p.
Publication Year :
2004

Abstract

In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14690268
Volume :
4
Issue :
4
Database :
Academic Search Index
Journal :
International Journal of Computational Intelligence & Applications
Publication Type :
Academic Journal
Accession number :
16719242
Full Text :
https://doi.org/10.1142/S1469026804001379