关键词: 三支聚类, 邻域, k-means聚类, k-medoid聚类, fuzzy c-means聚类

Abstract: Traditional clustering methods are two-way clustering which assumes that a cluster must be represented by a set with crisp boundary. However, assigning uncertain elements into a cluster will increase decision risk. Three-way clustering puts the identified elements into the core region and the uncertain elements into the fringe region to reduce decision risk. This paper presents a strategy for converting a two-way cluster to three-way cluster using the [ε] neighborhood of the samples. The method shrinks the two-way clustering result to get the core region and expands the two-way clustering result to get the fringe region. The experiments using the proposed method on UCI data sets show that the strategy is effective in reducing the Davies-Bouldin-Index and increasing the average silhouette coefficient and accuracy of clustering results.

Key words: three-way clustering, neighborhood, k-means clustering, k-medoid clustering, fuzzy c-means clustering