基于互包含度的数据分类效果评价研究

Study on Evaluating Data Classifying Quality Based on Mutual Subsethood

针对模糊C-均值聚类算法对初始化分类参数的选择比较敏感而导致分类结果差异性较大的不足,提出了基于互包含度的有效性函数进行数据分类效果好坏的评价。实验结果表明,本文定义的分类效果评价方法是可行的。

Based on the shortage of fuzzy c-means algorithm which initialized classification parameter is sensitivity to data classifying quality,and different initialized classification parameters generate classifying result with bigger other- ness. A new evaluating criterion based on mutual subsethood puts forward to assess data classifying quality in this pa- per. Experimental results show that an evaluating criterion proposed in this paper is feasible.