Approaches to cluster validity index via mahalanobis metric
Yan Ren, Lidong Wang, Wei Guan · 2015
DBCAMM is a novel density based clustering algorithm via using the Mahalanobis metric, which can extract the traditional clustering information and the intrinsic clustering structure. However, one of the most significant further work of DBCAMM is to develop a cluster validity index which can indicate how to select the parameters in the algorithm. Thus, new cluster validity index IRY is proposed via using the Mahalanobis metric for the validation of partitions of object data produced by DBCAMM algorithm. The proposed index is tested and validated using several synthetic datasets with arbitrary shape. The results of the comparisons show the superior effectiveness and reliability of the proposed index in comparison to the results of other cluster validity index for FCM clustering algorithm.