Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (10): 197-203.doi: 10.11896/jsjkx.200900061

• Database & Big Data & Data Science • Previous Articles     Next Articles

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

CUI Guo-nan1, WANG Li-song1, KANG Jie-xiang2, GAO Zhong-jie2, WANG Hui2, YIN Wei2   

  1. 1 School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210000,China
    2 Software Department of China Aeronautical Radio Electronics Research Institute,Shanghai 200233,China
  • Received:2020-09-08 Revised:2021-03-22 Online:2021-10-15 Published:2021-10-18
  • About author:CUI Guo-nan,born in 1996,postgra-duate.His main research interests include multi-object optimization method and data mining.
    WANG Li-song,born in 1969,associate professor,is a member of China Computer Federation.His main research interests include avionics safety analysis,data management in distributed environments,formal methods and model-based safety analysis,and wireless sensor network.
  • Supported by:
    National Natural Science Foundation of China(J19K004).

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Abstract: 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,OSACF establishes a bi-objective model between cluster number k and clustering validity index,and uses MOEA to optimize the bi-objective model to determine the optimal cluster number k,so as to avoid the influence of monotonous decreasing of clustering validity index.On the other hand,OSACF uses morphological similarity distance to replace the traditional Euclidean distance metric,which avoids the influence of cluster shape on the calculation of cluster k.The experimental results show that the optimal fuzzy cluster number k obtained by OSACF combined with MOEA is more accurate than the results obtained by the existing clustering effectiveness indicators.

Key words: Clustering validity index, Fuzzy clustering, Multi-objective optimization algorithm, Number of clusters k

CLC Number: 

  • TP302
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