FO-CA:一种基于距离差异度组合权重的多属性数据分类方法

FO-CA:A Multiple Attribute Data Classification Method Based on Distance Difference Degree Combination Weighting

为了解决多属性数据分类问题,提出了一种基于模糊优选模型与聚类分析的分类方法(FO-CA)。首先由模糊优选模型得到有序综合指标数据集,其中在权重阶段提出了距离差异度并以此为依据构建了一种组合主客观权重的赋权方法;然后采用聚类分析将有序综合指标数据集聚类为几个簇进而分类;最后选取UCI中的Iris、Wine和Ruspini 3个数据集进行仿真实验。实验结果表明,该分类方法相比模糊优选方法及K-Means算法能获得更好的分类结果,对决策者有一定的参考价值。

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 KMeans algorithm,and provides an effective approach for data classification.