ALID: Scalable dominant cluster detection

Lingyang Chu, Shuhui Wang, Siyuan Liu, Qingming Huang, Jian Pei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 Evolutionary Game Theory (EGT) and develop a scalable algorithm, Approximate Localized Infection Immunization Dynamics (ALID). The major idea is to perform Localized Infection Immunization Dynamics (LID) to find dense subgraphs within local ranges of the affinity graph. LID is further scaled up with guaranteed high efficiency and detection quality by an estimated Region of Interest (ROI) and a Candidate Infective Vertex Search method (CIVS). ALID only constructs small local affinity graphs and has time complexity O(C(a*+ δ)n) and space complexity O(a*(a*+δ)), where a*is the size of the largest dominant cluster, and C < n and δ < n are small constants. We demonstrate by extensive experiments on both synthetic data and real world data that ALID achieves the state-of-theart detection quality with much lower time and space cost on single machine. We also demonstrate the encouraging parallelization performance of ALID by implementing the Parallel ALID (PALID) on Apache Spark. PALID processes 50 million SIFT data points in 2.29 hours, achieving a speedup ratio of 7.51 with 8 executors.

Original languageEnglish (US)
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Pages826-837
Number of pages12
Volume8
Edition8
ISBN (Electronic)9780000000002
DOIs
StatePublished - 2015
Event41st International Conference on Very Large Data Bases, VLDB 2015 - Kohala Coast, United States
Duration: Aug 31 2015Sep 4 2015

Other

Other41st International Conference on Very Large Data Bases, VLDB 2015
CountryUnited States
CityKohala Coast
Period8/31/159/4/15

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)

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  • Cite this

    Chu, L., Wang, S., Liu, S., Huang, Q., & Pei, J. (2015). ALID: Scalable dominant cluster detection. In Proceedings of the VLDB Endowment (8 ed., Vol. 8, pp. 826-837). Association for Computing Machinery. https://doi.org/10.14778/2757807.2757808