Geometric divergence based fuzzy clustering with strong resilience to noise features
Article Type
Research Article
Publication Title
Pattern Recognition Letters
Abstract
In this article we consider the problem of fuzzy partitional clustering using a separable multi-dimensional version of the geometric distance which includes f-divergences as special cases. We propose an iterative relocation algorithm for the Fuzzy C Means (FCM) clustering that is guaranteed to converge to local minima. We also demonstrate, through theoretical analysis, that the FCM clustering with the proposed divergence based similarity measure, is more robust towards the perturbation of noise features than the standard FCM with Euclidean distance based similarity measure. In addition, we show that FCM with the suggested geometric divergence measure has better or comparable clustering performance to that of FCM with squared Euclidean distance on real world and synthetic datasets (even in absence of the noise features).
First Page
60
Last Page
67
DOI
10.1016/j.patrec.2016.04.013
Publication Date
8-1-2016
Recommended Citation
Saha, Arkajyoti and Das, Swagatam, "Geometric divergence based fuzzy clustering with strong resilience to noise features" (2016). Journal Articles. 4234.
https://digitalcommons.isical.ac.in/journal-articles/4234