A new cluster validity index is proposed for fuzzy clusters obtained from fuzzy c-means algorithm. The proposed validity index exploits inter-cluster proximity between fuzzy clusters. Inter-cluster proximity is used to measure the degree of overlap between clusters. A low proximity value refers to well-partitioned clusters. The best fuzzy c-partition is obtained by minimizing inter-cluster proximity with respect to c. Well-known data sets are tested to show the effectiveness and reliability of the proposed index.

翻译：本文针对模糊C均值算法得到的模糊聚类，提出了一种新的聚类有效性指标。该有效性指标利用了模糊聚类之间的类间邻近度。类间邻近度用于衡量聚类之间的重叠程度，较低的邻近度值表示聚类划分良好。通过针对聚类数c最小化类间邻近度，可以获得最优的模糊C划分。通过对多个经典数据集的测试，验证了所提出指标的有效性和可靠性。