一种基于模糊度的聚类有效性函数

Cluster Validity Function Based on Fuzzy Degree

根据模糊集理论,结合模糊C-均值聚类算法的约束条件.提出聚类模糊集概念.定义聚类模糊度.通过深入分析聚类模糊集的模糊度和贴近度在聚类评价中的作用,设计出一种模糊聚类有效性函数,并给出应用该函数实现模糊C-均值聚类有效性判定的具体步骤.实验结果表明,本文提出的聚类有效性函数是合理的.

Construction of cluster validity function is a commonly used method to determine the optimal partition and optimal number of clusters for fuzzy partitions. Based on the basic theory of fuzzy set, the notion of cluster fuzzy set is suggested, which is subjected to the constraint conditions of fuzzy C-means cluster algorithm. The cluster fuzzy degree and the lattice degree of approaching for cluster fuzzy set are defined and their functions in validation process of fuzzy clustering are deeply analyzed. A new cluster validity function is presented, in which two factors, the cluster fuzzy degree and the lattice degree of approaching, are taken into account comprehensively. Furthermore, the detailed steps are given to apply the cluster validity function to the clustering validity for the fuzzy C- means cluster algorithm. The experimental results indicate the effectiveness and robustness of the proposed cluster validity function.