Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering
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説明
<jats:p>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.</jats:p>
収録刊行物
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 12 (5), 448-453, 2008-09-20
Fuji Technology Press Ltd.