Abstract
A swarm-intelligence-inspired approach to clustering data is described. The algorithm consists of two stages. 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 (FCM) criterion. In the second stage, the best cluster centers found are used as the initial cluster centers for the FCM algorithm. Results on 18 data sets show that the partitions found using the ant initialization are better optimized than those obtained from random initializations. The use of a reformulated fuzzy partition validity metric as the optimization criterion is shown to enable determination of the number of cluster centers in the data for several data sets. Hard C-means (HCM) was also used after reformulation, and the partitions obtained from the ant-based algorithm were better optimized than those from randomly initialized HCM.
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