On a Family of New Sequential Hard Clustering
Yukihiro Hamasuna, Yasunori Endo · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2015
This paper presents a new algorithm of sequential cluster extraction based on hardc-means and hardc-medoids clustering. Sequential cluster extraction means that the algorithm extracts ‘one cluster at a time.’ A characteristic parameter, called a noise parameter, is used in noise clustering based sequential clustering. We propose a novel sequential clustering method called new sequential clustering, extracts an arbitrary number of objects as one cluster by considering the noise parameter as a variable to be optimized. Experimental results with four data sets confirm the effectiveness of our proposal. These results also show that classification results strongly depend on parameter ν and that our proposal is applicable to the first stage in a two-stage clustering algorithm.