Fuzzy ant clustering by centroid positioning
Parag M. Kanade, Lawrence Hall · 2005
We present a swarm intelligence based algorithm for data clustering. The algorithm uses ant colony optimization principles to find good partitions of the data. In the first stage of the algorithm ants move the cluster centers in feature space. The cluster centers found by the ants are evaluated using a reformulated fuzzy c-means criterion. In the second stage the best cluster centers found are used as the initial cluster centers for the fuzzy c-means (FCM) algorithm. Results on 8 datasets show that the partitions found by FCM using the ant initialization are better optimized than those from randomly initialized FCM. Hard c-means was also used in the second stage and the partitions from the algorithm are better optimized than those from randomly initialized hard c-means.