Fuzzy Clustering Ensemble Considering Cluster Dependability

Abstract

Clustering ensemble has been progressively popular in the ongoing years by combining several base clustering methods into a most likely better and increasingly robust one. Nonetheless, fuzzy clustering dependability (durability) has been unnoticed within the majority of the proposed clustering ensemble approach. This makes them weak against low-quality fuzzy base clusters. In spite of a few endeavors made to the clustering methods, it appears that they consider each base-clustering separately without considering its local diversity. In this paper, to compensate for the mentioned weakness a new fuzzy clustering ensemble approach has been proposed using a weighting strategy at fuzzy cluster level. Indeed, each fuzzy cluster has a contribution weight computed based on its reliability (dependability/durability). After computing fuzzy cluster dependability (reliability/durability), dependability based fuzzy cluster-wise weighted matrix (DFCWWM) is computed. As a final point, the final clustering is obtained by applying the FCM traditional clustering algorithm over DFCWWM. The time complexity of the proposed approach is linear in terms of the number of data-points. The proposed approach has been assessed on 15 various standard datasets. The experimental evaluation has indicated that the proposed method has better performance than the state-of-the-art methods.