Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering
Sadaaki Miyamoto, Youhei Kuroda, Kenta Arai · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2008
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.