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
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.
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