Three-way density peak clustering in incomplete information systems

Abstract

The absence of data in the information systems will result in uncertainty in the classification of objects, and the fringe region of the cluster in the three-way clustering reflects the uncertainty of the clustering results, which can fit the incomplete information system well. Consequently, this paper proposes a three-way clustering framework in incomplete information systems, drawing on the density peak clustering algorithm and the model of three-way decision. Firstly, this paper defines the reflexive binary relation in incomplete information systems and determines the center of the corresponding object class. Then, the density peak clustering algorithm is utilized to identify the optimal clustering centers among all object class centers. Subsequently, the membership degree and relative loss function matrix of the object under each cluster center are defined according to the distance relationship between each object and all cluster centers. Finally, the clustering rules are obtained by the minimum risk decision theory, and the initial clustering results are processed to meet the three-way clustering criteria. In the experimental section of this paper, two sets of experiments are designed to show the clustering accuracy of the proposed algorithm and the influence of parameters on the clustering results.