基于模糊划分测度的聚类有效性指标

聚类有效性指标用于评价聚类结果的有效性。根据聚类的基本特性，提出了一个新的用于发现最优模糊划分的聚类有效性指标，该有效性指标采用模糊划分测度和信息熵两个重要因子来评价模糊聚类的有效性。其中，模糊划分测度用于评价聚类的类内紧致性与类间分离性，而信息熵则反映了模糊聚类划分结果的不确定性程度。实验结果表明，该聚类有效性指标能对模糊聚类结果的有效性进行正确的评价，特别是对于空间数据的聚类有效性评价，同其他有效性指标相比，它不仅能得到最优的模糊划分，而且对权重系数也是不敏感的。

Cluster Validity Index Based on Measure of Fuzzy Partition

Cluster validity index is used to evaluate the validity of clustering.Anew cluster validity index is proposed to identify the optimal fuzzy partition according to the basic properties of clustering.The index exploits two important evaluation factors: the measure of fuzzy partition and information entropy.The first factor is used to evaluate the compress within a cluster and the separation between clusters,and the second is to measure the uncertainty of the partition result.The experimental results indicate that the index is effective and efficient for evaluating the result of fuzzy clustering.Especially,for the spatial data,the index can correctly identify the optimal clustering number and is not sensitive to the weighting exponent.