Towards Parameter-less and Similarity-based Fuzzy Clustering based on PCM Method
Vincent S. M. Tseng, Ching-Pin Kao · 2006
The fuzzy clustering algorithms have been applied in a wide variety of fields. In this paper, we propose a novel fuzzy clustering method named Similarity-based PCM (SPCM), which is parameter-less and suitable for similarity-based clustering applications. The main idea behind SPCM is to integrate PCM clustering with the Mountain Method (MM) such that the good fuzzy clustering result can be generated automatically without requesting users to specify parameters like the cluster number. This complements the deficiency of other existing relational fuzzy clustering methods when applied to similarity-based clustering applications. For example, FANNY, RFCM, NERFCM, and FRC request the specification of the number of clusters and are severely sensitive to outliers. Although R-RFCM, R-NERFCM, and R-FRC are robust in noisy environments, they request the specification of the number of clusters and require good initialization. Through performance evaluation on both of real and synthetic data sets, the SPCM is shown to perform excellently in clustering quality with various kinds of similarity measures, even in a noisy environment with outliers. Therefore, the SPCM can serve as a promising method for parameter-less and similarity-based fuzzy clustering applications.