Grouping points by shared subspaces for effective subspace clustering

- Zhu, Ye, Ting, Kaiming, Carman, Mark

Title
Grouping points by shared subspaces for effective subspace clustering
Creator
Zhu, Ye; Ting, Kaiming; Carman, Mark
Date
2018
Type
Text; Journal article
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/167687
Identifier
vital:13716
Identifier
https://doi.org/10.1016/j.patcog.2018.05.027
Identifier
ISBN:0031-3203
Abstract
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.
Publisher
Elsevier Ltd
Relation
Pattern Recognition Vol. 83, no. (2018), p. 230-244
Rights
Copyright © 2018 Elsevier Ltd
Rights
This metadata is freely available under a CCO license
Subject
0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 0906 Electrical and Electronic Engineering; Subspace clustering; Shared subspaces; Density-based clustering
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