A fast hierarchical clustering approach based on partition and merging scheme

Yiqun Zhang, Yiu Ming CHEUNG

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Hierarchical clustering is one major kind of clustering approaches. As far as we know, given n data points, the time complexity of most existing hierarchical clustering approaches is O(n). Although some state-of-the-art fast hierarchical clustering approaches have lower time complexity, their clustering accuracy is sacrificed and sensitive to some certain data distribution types. This paper therefore presents a partition-and-merging scheme for fast hierarchical clustering, which divides data objects into proper groups and merges them within their groups to save computation cost. Since both spatial distance and density difference, which contain local and global distribution information of data, are considered in the merging stage, the proposed approach has outstanding performance in terms of effectiveness, efficiency and robustness. Experimental results show the promising results in comparison with the existing counterparts.

Original languageEnglish
Title of host publicationProceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages846-851
Number of pages6
ISBN (Electronic)9781538643624
DOIs
Publication statusPublished - 8 Jun 2018
Event10th International Conference on Advanced Computational Intelligence, ICACI 2018 - Xiamen, Fujian, China
Duration: 29 Mar 201831 Mar 2018

Publication series

NameProceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018

Conference

Conference10th International Conference on Advanced Computational Intelligence, ICACI 2018
Country/TerritoryChina
CityXiamen, Fujian
Period29/03/1831/03/18

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Modelling and Simulation
  • Control and Optimization

User-Defined Keywords

  • Competitive learning
  • Hierarchial clustering
  • Partition and merging scheme
  • Unsupervised learning

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