A novel clustering ensemble model based on granular computing

Clustering ensemble is one of the popular methods in the field of data mining for discovering hidden patterns in unlabeled datasets. Researches have shown that selecting base clustering results with certain differences and high quality to participate in the fusion process can improve the quality of the final result. However, the existing inherent characteristics of uncertainty, ambiguity, and overlap of the base clustering results make the selection of the base clustering members more difficult. The accuracy of the final results is easily disturbed by low-quality base clustering members. From the perspective of granular computing, a novel clustering ensemble model is proposed. The similarity among ensemble members is measured by granularity distance, so the quality of the base clustering results is ensured meanwhile the difference among them is enlarged, which is beneficial to improve the accuracy of the final result. According to the dividing ability of knowledge granularity, the method of elements generation for the co-association matrix is optimized and improved. The results obtained from the improved sample similarity measurement are more consistent with the structure of the real data. Compared with the traditional single clustering algorithm and some popular clustering ensemble methods, experiments show that the proposed model improves the quality of the final clustering result and has good expandability.