D3WC: Deep three-way clustering with granular evidence fusion

Abstract

Deep clustering has gained significant traction as an unsupervised learning method, demonstrating considerable success in processing high-dimensional samples in data mining and computer vision. However, the ambiguity of high-dimensional data presents a challenge for deep clustering, which struggles to manage data uncertainty directly. In addition, while similarities and correlations in data often concentrate in local neighborhoods, traditional deep clustering methods frequently overlook these local relationships. To overcome these limitations, this paper presents a novel deep three-way clustering with granular evidence fusion. First, a fused contrastive deep FCM clustering network framework is introduced to project data from complex original data space to a more suitable deep feature space. Second, drawing upon the principles of three-way decision, the clustering results of the first stage are divided into positive, boundary, and negative regions, effectively addressing data uncertainty. Finally, a novel semiball neighborhood granulation method is employed to construct information granules for uncertain samples. This paper further leverages evidence theory to integrate belief information in these information granules, facilitating the redistribution of uncertain data. By emphasizing local structures, the proposed method effectively describes the characteristics of complex data. Experimental results confirm the effectiveness of this approach, showcasing its ability to enhance the clustering process.