Impact of Exponent Parameter Value for the Partition Matrix on the Performance of Fuzzy C Means Algorithm

Soft Clustering plays a very important rule on clustering real world data where a data item contributes to more than one cluster. Fuzzy logic based algorithms are always suitable for performing soft clustering tasks. Fuzzy C Means (FCM) algorithm is a very popular fuzzy logic based algorithm. In cas...

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Bibliographic Details
Published inarXiv.org
Main Authors Dibya Jyoti Bora, Gupta, Anil Kumar
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.06.2014
Subjects
Algorithms
Clustering
Fuzzy logic
Matrix partitioning
Parameters
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Summary:Soft Clustering plays a very important rule on clustering real world data where a data item contributes to more than one cluster. Fuzzy logic based algorithms are always suitable for performing soft clustering tasks. Fuzzy C Means (FCM) algorithm is a very popular fuzzy logic based algorithm. In case of fuzzy logic based algorithm, the parameter like exponent for the partition matrix that we have to fix for the clustering task plays a very important rule on the performance of the algorithm. In this paper, an experimental analysis is done on FCM algorithm to observe the impact of this parameter on the performance of the algorithm.
ISSN:2331-8422