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Entropy‐based metrics in swarm clustering
Author(s) -
Liu Bo,
Pan Jiuhui,
McKay R. I. Bob
Publication year - 2009
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20374
Subject(s) - cluster analysis , entropy (arrow of time) , computer science , data mining , correlation clustering , artificial intelligence , mathematics , algorithm , physics , quantum mechanics
Ant‐based 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 introduced an entropy metric into an ant‐based clustering algorithm and compared it with other closely related algorithms using local density. The results strongly support the value of entropy metrics, obtaining faster and more accurate results. Entropy governs the pickup 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 investigated Ramos and Abraham's (In: Proc 2003 IEEE Congr Evol Comput, Hoboken, NJ: IEEE Press; 2003. pp 1370–1375) 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 nonincremental approaches to data clustering. © 2009 Wiley Periodicals, Inc.