Swarm Based Fuzzy Clustering with Partition Validity

Swarm based approaches to clustering have been shown to be able to skip local extrema by doing a form of global search. We previously reported on the use of a swarm based approach using artificial ants to do fuzzy clustering by optimizing the fuzzy c-means (FCM) criterion. FCM requires that one choose the number of cluster centers (c). In the event that the user of the algorithm is unsure of the number of cluster centers, they can try several different choices and evaluate them with a cluster validity metric. In this work, we use a fuzzy cluster validity metric proposed by Xie and Beni as the criterion for evaluating a partition produced by swarm based clustering. Interestingly, when provided with more clusters than exist in the data our ant-based approach produces a partition with empty clusters and/or very lightly populated clusters. We used two data sets, Iris and an artificially generated data set, to show that optimizing a cluster validity metric with a swarm based approach can effectively provide an indication of how many clusters there are in the data