Interval competitive agglomeration clustering algorithm

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 t...

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Bibliographic Details
Published in:Expert systems with applications 2010-09, Vol.37 (9), p.6567-6578
Main Authors: Jeng, Jin-Tsong, Chuang, Chen-Chia, Tao, C.W.
Format: Article
Language:English
Subjects:
Agglomeration
Algorithms
Clustering
Clusters
Expert systems
Fuzzy c-means clustering algorithm
Interval competitive agglomeration clustering algorithm
Intervals
Optimization
Symbolic interval-values data
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Summary: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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2010.02.129