The possibility based clustering algorithm PCM was first proposed by Krishnapuram and Keller to overcome the noise sensitivity of algorithm FCM (Fuzzy C-Means). However, PCM still suffers from the following weaknesses: (1) the clustering results are strongly dependent on parameter selection and/or initialization; (2) the clustering accuracy is often deteriorated due to its coincident clustering problem; (3) outliers can not be well labeled, which will weaken its clustering performances in real applications. In this study, in order to effectively avoid the above weaknesses, a novel enhanced PCM version (EPCM) is presented. Here, at first a novel strategy of flexible hyperspheric dichotomy is proposed which may partition a dataset into two parts: the main cluster and auxiliary cluster, and is then utilized to construct the objective function of EPCM with some novel constraints. Finally, EPCM is realized by using an alternative optimization approach. The main advantage of EPCM lies in the fact that it can not only avoid the coincident cluster problem by using the novel constraint in its objective function, but also has less noise sensitivity and higher clustering accuracy due to the introduction of the strategy of flexible hyperspheric dichotomy. Our experimental results about simulated and real datasets confirm the above conclusions.
- Enhanced possibilistic c-Means clustering (EPCM)
- Flexible hyperspheric dichotomy
- Image segmentation
ASJC Scopus subject areas
- Theoretical Computer Science
- Geometry and Topology