Resumen
Fuzzy c-means (FCM), the fuzzy variant of the popular k-means, has been used for data clustering when cluster boundaries are not well defined. The choice of initial cluster prototypes (or the initialization of cluster memberships), and the fact that the number of clusters needs to be defined a priori are two major factors that can affect the performance of FCM. In this paper, we review algorithms and methods used to overcome these two specific drawbacks. We propose a new cooperative multi-population differential evolution method with elitism to identify near-optimal initial cluster prototypes and also determine the most optimal number of clusters in the data. The differential evolution populations use a smaller subset of the dataset, one that captures the same structure of the dataset. We compare the proposed methodology to newer methods proposed in the literature, with simulations performed on standard benchmark data from the UCI machine learning repository. Finally, we present a case study for clustering time-series patterns from sensor data related to real-time machine health monitoring using the proposed method. Simulation results are promising and show that the proposed methodology can be effective in clustering a wide range of datasets.