Swarm ascending: Swarm intelligence-based exemplar group detection for robust clustering

Younghoon Kim, Minjung Lee, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


An exemplar is a representative observation for each cluster. Exemplar-based clustering algorithms, which find the exemplars and assign data points to the nearest exemplar, have exhibited promising performance. However, the single- and multi-exemplar methods become inadequate for clustering data points with nonlinear and local patterns because one exemplar (or a set of sparse exemplars for a nonlinear cluster) is insufficient to represent the cluster. In this paper, we propose a swarm intelligence-based exemplar group detection method that ascends data points to local high-density points and groups the merged points. The proposed method is robust to nonlinear and local patterns because it detects the intrinsic structure of each cluster more sufficiently than sparse exemplars. We use simulation and real-world data to demonstrate the usefulness of the proposed method by comparing it to existing methods in terms of clustering accuracy. The comparison results demonstrate that the proposed method outperforms the alternatives.

Original languageEnglish
Article number107062
JournalApplied Soft Computing
Publication statusPublished - 2021 Apr

Bibliographical note

Funding Information:
The authors would like to thank the editor and reviewers for their useful comments and suggestions, which were helpful in improving the quality of the paper. This research was supported by BK 21 FOUR, the National Research Foundation of Korea grant funded by the Korea government (MSIT) (No. NRF-2019R1A4A1024732 ), the Institute for Information & Communications Technology Promotion grant funded by the Korea government (No. 2018–0-00440 and ITRC support program IITP-2020-0-01749 ), and the Ministry of Culture, Sports and Tourism , and the Korea Creative Content Agency ( R2019020067 ).

Publisher Copyright:
© 2020 Elsevier B.V.


  • Clustering
  • Exemplar group detection
  • Kernel density estimation
  • Swarm ascending
  • Swarm intelligence
  • Unsupervised learning

ASJC Scopus subject areas

  • Software


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