Enhancements of evidential [formula omitted]-means algorithms: A clustering framework via feature-weight learning

Abstract

As a core paradigm of the evidential clustering algorithm, evidential c-means (ECM) offers a more flexible credal partition to characterize uncertainty and imprecision in cluster assignment, which generalizes hard and fuzzy partitions. Nonetheless, ECM and its derivatives equally consider the importance of each feature in clustering, which can lead to suboptimal clustering performance, especially in high-dimensional datasets where some features are more informative than others. In this paper, we propose a series of new evidential clustering algorithms by integrating feature-weight learning to simultaneously characterize uncertainty and imprecision in cluster assignment and automatically learn the different importance of distinct features in clustering. We consider ECM with vector feature-weight learning and ECM with matrix feature-weight learning, and introduce two different constraints on feature weights, respectively. We develop four objective functions and derive the corresponding optimization process. Extensive experimental results using synthetic and real-world datasets demonstrate the feasibility and effectiveness of the proposed algorithms.