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

被引:51
作者
Azzag, Hanene
Venturini, Gilles
Oliver, Antoine
Guinot, Christiane
机构
[1] Univ Tours, Ecole Polytech, Lab Informat, F-37200 Tours, France
[2] CERIES, F-92521 Neuilly Sur Seine, France
关键词
metaheuristics; Internet; evolutionary computation; artificial ants; multi-agent systems;
D O I
10.1016/j.ejor.2005.03.062
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:906 / 922
页数:17
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