A swarm-inspired projection algorithm

Mu Chun Su, Shi Yong Su, Yu Xiang Zhao

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

In this paper a new data projection algorithm which was inspired by the foraging behaviors of doves is proposed. We name the new data projection the swarm-inspired projection (SIP) algorithm. The algorithm allows us to visually estimate the number of clusters existing in a data set. Based on the projection result, we may then partition the data set into the corresponding number of clusters. The SIP algorithm regards each data pattern in a data set as a piece of crumb which will be sequentially tossed to a flock of doves on the ground. The doves will adjust their physical positions to compete for crumbs. Gradually, the flock of doves will be divided into several groups according to the distributions of the crumbs. The formed groups will naturally correspond to the underlying data structures in the data set. By viewing the scatter plot of the final positions of the doves we can estimate the number of clusters existing in the data set. Several data sets were used to demonstrate the effectiveness of the proposed SIP algorithm.

Original languageEnglish
Pages (from-to)2764-2786
Number of pages23
JournalPattern Recognition
Volume42
Issue number11
DOIs
StatePublished - Nov 2009

Keywords

  • Cluster analysis
  • Neural networks
  • Projection
  • Self-organizing feature map
  • Swarm intelligence

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