Herd Clustering: A synergistic data clustering approach using collective intelligence

Kachun Wong, Chengbin Peng, Yue Li, Takming Chan

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Traditional data mining methods emphasize on analytical abilities to decipher data, assuming that data are static during a mining process. We challenge this assumption, arguing that we can improve the analysis by vitalizing data. In this paper, this principle is used to develop a new clustering algorithm. Inspired by herd behavior, the clustering method is a synergistic approach using collective intelligence called Herd Clustering (HC). The novel part is laid in its first stage where data instances are represented by moving particles. Particles attract each other locally and form clusters by themselves as shown in the case studies reported. To demonstrate its effectiveness, the performance of HC is compared to other state-of-the art clustering methods on more than thirty datasets using four performance metrics. An application for DNA motif discovery is also conducted. The results support the effectiveness of HC and thus the underlying philosophy. © 2014 Elsevier B.V.
Original languageEnglish (US)
Pages (from-to)61-75
Number of pages15
JournalApplied Soft Computing
StatePublished - Oct 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Software


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