关键词: 模糊优选,聚类分析,距离差异度,组合权重,分类

Abstract: In order to solve the problem of multi-attribute data classification,we proposed a classification method based on fuzzy optimization and clustering analysis (FO-CA)．First,we used fuzzy optimization model to get one dimensional composite indicator data set．Meanwhile,according to the distance difference degree,we established a combination weighting method to integrate subjective weights and objective weights in weighting stage．Second,we used hierarchical cluster analysis method to divide the composite indicator data set into several clusters,and then classified the clusters．Finally,we selected Iris,Wine and Ruspini datasets from UCI Machine Learning Repository for simulation experiments．The experiment results show that the proposed method achieves better results than fuzzy optimization method and K-Means algorithm,and provides an effective approach for data classification.

Key words: Fuzzy optimization,Clustering analysis,Distance difference degree,Combination weight,Classification