关键词: 谱聚类, 有效距离, 距离度量

Abstract: Based on existing distance metrics and the traditional spectral clustering algorithm, this paper proposes a new spectral clustering based on effective distance (SCED). Specifically, the proposed SCED algorithm uses effective distance to replace conventional Euclidean distance, by considering global properties of data that are reflected by sparse reconstruction coefficients. In effective distance, the similarity of a sample pair is evaluated by using not only the distance between these two samples, but also distances between one specific sample and other related samples. Sparse reconstruction coefficients are employed to reflect such global relationship among samples. The experimental results on ten UCI benchmark datasets demonstrate the efficiency of the proposed SCED algorithm.

Key words: spectral clustering, effective distance, distance metric