Basic Consideration of Spherical Rough Set C-Means Clustering and Its Application to Collaborative Filtering

Abstract

In recent years, with the increasing demand for big data utilization, the importance of clustering, which is a technique for automatically classifying and summarizing data without supervision, is growing. Rough clustering, which incorporates the perspective of rough set theory, is a clustering method designed to handle the uncertainty of each object’s membership to each cluster. Additionally, for certain clustering tasks, spherical clustering, which projects data onto a sphere and performs classification based on cosine similarity instead of Euclidean distance, proves effective. As an extension of the representative rough clustering algorithm, rough C-means (RCM), to spherical clustering, spherical RCM (SRCM) has been proposed. In this study, we extend rough set C-means (RSCM), a rough clustering method that considers granularity, to spherical clustering and propose spherical RSCM (SRSCM). Furthermore, as a benchmark, we apply each method to a collaborative filtering task and compare their recommendation performance.