A hierarchical ant based clustering algorithm and its use in three real-world applications

• Published in: European journal of operational research Vol. 179; no. 3; pp. 906 - 922
• Main Authors: Azzag, Hanene, Venturini, Gilles, Oliver, Antoine, Guinot, Christiane
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16-06-2007; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Algorithms; Artificial ants; Clustering; Evolutionary computation; Heuristic; Internet; Metaheuristics; Multi-agent systems; Studies; Internet; Metaheuristics; Artificial ants; Evolutionary computation; Multi-agent systems
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2005.03.062

In this paper is presented a new model for data clustering, which is inspired from the self-assembly behavior of real ants. Real ants can build complex structures by connecting themselves to each others. It is shown is this paper that this behavior can be used to build a hierarchical tree-structured partitioning of the data according to the similarities between those data. Several algorithms have been detailed using this model (called AntTree): deterministic or stochastic algorithms that may use or not global or local thresholds. Those algorithms have been evaluated using artificial and real databases. Our algorithms obtain competitive results when compared to the Kmeans, to ANTCLASS, and to Ascending Hierarchical Clustering. AntTree has been applied to three real world applications: the analysis of human healthy skin, the on-line mining of web sites usage, and the automatic construction of portal sites.