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Abstract

Detecting dominant clusters is important in many analytic applications. The state-of-the-art methods find dense subgraphs on the affinity graph as dominant clusters. However, the time and space complexities of those methods are dominated by the construction of affinity graph, which is quadratic with respect to the number of data points, and thus are impractical on large data sets. To tackle the challenge, in this paper, we apply …

Authors

Chu L; Wang S; Liu S; Huang Q; Pei J

Volume

8

Pagination

pp. 826-837

Publisher

Association for Computing Machinery (ACM)

Publication Date

April 2015

DOI

10.14778/2757807.2757808

Conference proceedings

Proceedings of the VLDB Endowment

Issue

8

ISSN

2150-8097