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dc.contributor.authorWong, Kachun
dc.contributor.authorPeng, Chengbin
dc.contributor.authorLi, Yue
dc.contributor.authorChan, Takming
dc.date.accessioned2015-08-03T12:09:34Z
dc.date.available2015-08-03T12:09:34Z
dc.date.issued2014-10
dc.identifier.citationWong, K.-C., Peng, C., Li, Y., & Chan, T.-M. (2014). Herd Clustering: A synergistic data clustering approach using collective intelligence. Applied Soft Computing, 23, 61–75. doi:10.1016/j.asoc.2014.05.034
dc.identifier.issn15684946
dc.identifier.doi10.1016/j.asoc.2014.05.034
dc.identifier.urihttp://hdl.handle.net/10754/563771
dc.description.abstractTraditional 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.
dc.publisherElsevier BV
dc.subjectCollective intelligence
dc.subjectHerd behavior
dc.subjectHeuristic
dc.subjectNatural computing
dc.titleHerd Clustering: A synergistic data clustering approach using collective intelligence
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalApplied Soft Computing
dc.contributor.institutionDepartment of Computer Science, University of Toronto, Toronto, ON, Canada
dc.contributor.institutionTerrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
dc.contributor.institutionDepartment of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, United States
kaust.personPeng, Chengbin


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