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Performance Analysis of Fuzzy c-means, Mountain and Subtractive Clustering Techniques

The Conventional used Fuzzy c-means clustering technique needs to be initialized manually with the number of clusters present in the data. Mountain clustering and Subtractive clustering overcome this by calculating the number of clusters automatically by analyzing data numerically. The purpose of this paper is to compare these three algorithms namely Mountain Clustering, Fuzzy C-means (FCM) and Subtractive Clustering. The experimental results are carried out on the synthetic datasets with varying distribution. The performances of these algorithms are evaluated on the basis of the regression analysis, position of the center, number of clusters and root mean square error. Keywords – Unsupervised clustering, subtractive clustering, mountain clustering, Fuzzy C-means.



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Keywords: clustering

ISSN: 0974-1771

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