Interval competitive agglomeration clustering algorithm

Authors:
Jin-Tsong Jeng;Chen-Chia Chuang;C. W. Tao
Affiliations:
Department of Computer Science and Information Engineering National Formosa University, No. 64, Wunhua Rd., Huwei Township, Yunlin County 632, Taiwan;Department of Electrical Engineering, National Ilan University, 1, Sec. 1, Shen-Lung Road, I-Lan 260, Taiwan;Department of Electrical Engineering, National Ilan University, 1, Sec. 1, Shen-Lung Road, I-Lan 260, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

In this study, an interval competitive agglomeration (ICA) clustering algorithm is proposed to overcome the problems of the unknown clusters number and the initialization of prototypes in the clustering algorithm for the symbolic interval-values data. In the proposed ICA clustering algorithm, both the Euclidean distance measure and the Hausdorff distance measure for the symbolic interval-values data are independently considered. Besides, the advantages of both hierarchical clustering algorithm and partitional clustering algorithm are also incorporated into the ICA clustering algorithm. Hence, the ICA clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments with simply data sets and real data sets show the merits and usefulness of the ICA clustering algorithm for the symbolic interval-values data.