NDLI: Efficient approaches for summarizing subspace clusters into k representatives

Content Provider	Springer Nature Link
Author	Chen, Guanhua Ma, Xiuli Yang, Dongqing Tang, Shiwei Shuai, Meng Xie, Kunqing
Copyright Year	2010
Abstract	A major challenge in subspace clustering is that subspace clustering may generate an explosive number of clusters with high computational complexity, which severely restricts the usage of subspace clustering. The problem gets even worse with the increase of the data’s dimensionality. In this paper, we propose to summarize the set of subspace clusters into k representative clusters to alleviate the problem. Typically, subspace clusters can be clustered further into k groups, and the set of representative clusters can be selected from each group. In such a way, only the most representative subspace clusters will be returned to user. Unfortunately, when the size of the set of representative clusters is specified, the problem of finding the optimal set is NP-hard. To solve this problem efficiently, we present two approximate methods: PCoC and HCoC. The greatest advantage of our methods is that we only need a subset of subspace clusters as the input instead of the complete set of subspace clusters. Precisely, only the clusters in low-dimensional subspaces are computed and assembled into representative clusters in high-dimensional subspaces. The approximate results can be found in polynomial time. Our performance study shows both the effectiveness and efficiency of these methods.
Starting Page	845
Ending Page	853
Page Count	9
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	15
Issue Number	5
e-ISSN	14337479
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-03-09
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Subspace clustering Representative clusters High-dimensional data Data summarization Data mining Control , Robotics, Mechatronics Artificial Intelligence (incl. Robotics) Computational Intelligence Mathematical Logic and Foundations
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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