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JACIII Vol.12 No.5 pp. 448-453
doi: 10.20965/jaciii.2008.p0448
(2008)

Paper:

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Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering

Sadaaki Miyamoto*, Youhei Kuroda, and Kenta Arai

*Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba
Ibaraki 305-8573, Japan

Received:
October 10, 2007
Accepted:
February 15, 2008
Published:
September 20, 2008
Creative Commons License
Keywords:
possibilistic clustering, sequential extraction of clusters, mountain clustering
Abstract
In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering's similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.
Cite this article as:
S. Miyamoto, Y. Kuroda, and K. Arai, “Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.5, pp. 448-453, 2008.
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