THRESHOLD ACCEPTING BASED FUZZY CLUSTERING ALGORITHMS

Abstract

In this paper, two new fuzzy clustering algorithms are proposed based on the global optimization metaheuristic, Threshold Accepting. Their effectiveness is demonstrated in the case of five well-known medium sized data sets viz. Iris, Wine, Glass, E.Coli and Olive oil and a large data set Thyroid. In terms of the least objective functions value, these algorithms named TAFC-1 (Threshold Accepting based Fuzzy Clustering) and TAFC-2 outperformed the well-known Fuzzy C-Means (FCM) algorithm in the case of 4 data sets and in the remaining two data sets, FCM marginally outperformed the TAFC. Xie-Beni cluster validity index is used in arriving at the 'optimal' number of clusters for all the algorithms. Here a novel strategy is proposed whereby the FCM is invoked to find alternative decision vectors whenever the neighbourhood search fails in its pursuit. This hybrid scheme has worked well. In conclusion, these new algorithms can be used as viable and efficient alternatives to the FCM algorithm.