Entropy-based metrics in swarm clustering

Ant-based clustering clustering methods have received significant attention as robust methods for clustering. Most ant-based algorithms use local density as a metric for determining the ants’ propensities to pick up or deposit a data item; however a number of authors in classical clustering methods have pointed out the advantages of entropy-based metrics for clustering. We introduce an entropy metric into an ant-based clustering algorithm, and compare it with other closelyrelated algorithms using local density. The results strongly support the value of entropy metrics, obtaining faster and more accurate results. Entropy governs the pick up and drop behaviors, while movement is guided by the density gradient. Entropy measures also require fewer training parameters than density-based clustering. The remaining parameters are subjected to robustness studies, and a detailed analysis is performed. In the second phase of the study, we further investigate Ramos and Abraham’s1 contention that ant-based methods are particularly suited to incremental clustering. Contrary to expectations, we did not find substantial differences between the efficiencies of incremental and non-incremental approaches to data clustering.