Fuzzy Clustering Validity Index Combined with Multi-objective Optimization Algorithm and Its Application

Fuzzy clustering method can analyze complex data sets more effectively.Because there are many kinds of fuzzy clustering algorithms and the clustering results will change with the number of input clusters, the results of fuzzy clustering algorithm are not accurate, so the number of fuzzy clustering k must be determined in order to obtain certain clustering results.At present, the existing research mainly uses a variety of fuzzy clustering effectiveness indexes to determine the optimal number of clusters k.However, fuzzy clustering indexes such as SSD,PBM will decrease monotonically with the increase of clustering number k,which makes it impossible to determine the optimal number of clusters k.Therefore, this paper proposes a fuzzy clustering validity index（OSACF） combined with a multi-objective optimization algorithm, which combines fuzzy clustering validity with a multi-objective optimization algorithm（MOEA） to solve the optimal number of clusters k problem.Different from using clustering validity index, OSAC