A new validity measure for fuzzy c-means clustering
This work addresses the need for better evaluation metrics in fuzzy clustering, but it appears incremental as it builds on existing fuzzy c-means methods.
The authors tackled the problem of evaluating fuzzy clustering partitions by proposing a new validity index based on inter-cluster proximity to measure overlap, and they demonstrated its effectiveness on well-known datasets.
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.