Generalized picture distance measure and applications to picture fuzzy clustering - Son, Le Hoang (Applied soft computing, 2016)
[Display omitted] •We focused on the clustering problem in picture fuzzy sets.•A generalized picture distance measure was proposed.•A novel hierarchical picture clustering method based on the measure was presented.•Its clustering quality is better than those of relevant clustering algorithms.•A clustering system for picture fuzzy sets was designed. Picture fuzzy set (PFS), which is a generalization of traditional fuzzy set and intuitionistic fuzzy set, shows great promises of better adaptation to many practical problems in pattern recognition, artificial life, robotic, expert and knowledge-based systems than existing types of fuzzy sets. An emerging research trend in PFS is development of clustering algorithms which can exploit and investigate hidden knowledge from a mass of datasets. Distance measure is one of the most important tools in clustering that determine the degree of relationship between two objects. In this paper, we propose a generalized picture distance measure and integrate it to a novel hierarchical picture fuzzy clustering method called Hierarchical Picture Clustering (HPC). Experimental results show that the clustering quality of the proposed algorithm is better than those of the relevant ones.