DPFCM: A novel distributed picture fuzzy clustering method on picture fuzzy sets

• We focused on fuzzy clustering in distributed environments.
• A novel distributed picture fuzzy clustering method was presented.
• It combined the ideas of the facilitator model and picture fuzzy sets.
• It was experimentally validated on benchmark datasets of UCI Machine Learning.
• It has better clustering quality than other relevant algorithms.

Fuzzy clustering is considered as an important tool in pattern recognition and knowledge discovery from a database; thus has been being applied broadly to various practical problems. Recent advances in data organization and processing such as the cloud computing technology which are suitable for the management, privacy and storing big datasets have made a significant breakthrough to information sciences and to the enhancement of the efficiency of fuzzy clustering. Distributed fuzzy clustering is an efficient mining technique that adapts the traditional fuzzy clustering with a new storage behavior where parts of the dataset are stored in different sites instead of the centralized main site. Some distributed fuzzy clustering algorithms were presented including the most effective one – the CDFCM of Zhou et al. (2013). Based upon the observation that the communication cost and the quality of results in CDFCM could be ameliorated through the integration of a distributed picture fuzzy clustering with the facilitator model, in this paper we will present a novel distributed picture fuzzy clustering method on picture fuzzy sets so-called DPFCM. Experimental results on various datasets show that the clustering quality of DPFCM is better than those of CDFCM and relevant algorithms.