Grouping points by shared subspaces for effective subspace clustering

PATTERN RECOGNITION

卷:83

Article

Zhu, Ye¹  Ting, Kai Ming²  Carman, Mark J.³ 

[1] Deakin Univ, Sch Informat Technol, Geelong, Vic 3125, Australia

[2] Federat Univ, Sch Engn & Informat Technol, Ballarat, Vic 3842, Australia

[3] Monash Univ, Fac Informat Technol, Clayton, Vic 3800, Australia

关键词: Subspace clustering;    Shared subspaces;    Density-based clustering;   

DOI  :  10.1016/j.patcog.2018.05.027

来源: Elsevier

【 摘 要 】

Clusters may exist in different subspaces of a multidimensional dataset. Traditional full-space clustering algorithms have difficulty in identifying these clusters. Various subspace clustering algorithms have used different subspace search strategies. They require clustering to assess whether cluster(s) exist in a subspace. In addition, all of them perform clustering by measuring similarity between points in the given feature space. As a result, the subspace selection and clustering processes are tightly coupled. In this paper, we propose a new subspace clustering framework named CSSub (Clustering by Shared Subspaces). It enables neighbouring core points to be clustered based on the number of subspaces they share. It explicitly splits candidate subspace selection and clustering into two separate processes, enabling different types of cluster definitions to be employed easily. Through extensive experiments on synthetic and real-world datasets, we demonstrate that CSSub discovers non-redundant subspace clusters with arbitrary shapes in noisy data; and it significantly outperforms existing state-of-the-art subspace clustering algorithms. (C) 2018 Elsevier Ltd. All rights reserved.

【 授权许可】

Free