Fuzzy Ensemble Clustering Based on Self-Coassociation and Prototype Propagation

Fuzzy clustering ensemble that combines multiple fuzzy clustering results can obtain more robust, novel, stable, and consistent clustering result. The research about fuzzy clustering ensemble is still in the initial stage. Due to the special information expression, excellent clustering ideas are not well-practiced in fuzzy clustering ensemble and the performance of fuzzy clustering ensemble still has a large improvement space. In data clustering, prototype-based clustering is effective and efficient. The main idea of prototype-based clustering is discovering prototype samples to represent clusters and assigning samples to the represented clusters. In this article, we draw the idea of prototype-based clustering to fuzzy clustering ensemble and handle the problems of how to discover prototype samples based on a set of fuzzy clustering results and how to assign the samples without accessing the original data features. First, we propose a self-coassociation measure of a sample and discover its natural ability to evaluate the sample's local density. The rationality of the prototype samples discovered based on self-coassociation is theoretically analyzed and visually shown on eight artificial data sets. Then, we propose a prototype propagation method to assign data samples gradually. The working mechanism of the proposed sample assignment method is visually shown in the image segmentation scene. Finally, we develop a fuzzy clustering ensemble method based on self-coassociation and prototype propagation. The effectiveness of the proposed method is illustrated by comparing it with eight representative methods on benchmark datasets.