A data clustering algorithm based on mussels wandering optimization

Peng Yan, Shi Yao Liu, Qi Kang, Bing Yao Huang, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

As an unsupervised learning method, clustering methods plays an important role in quality data mining and various other applications. This work investigates them based on swarm intelligence, introduces a new intelligence algorithm called mussels wandering optimization (MWO) to the data clustering field, and proposes a new clustering algorithm by combining K-means clustering method and MWO. Tests on six standard data sets are performed. The results demonstrate the validity and superiority of the proposed method over some representative clustering ones.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
PublisherIEEE Computer Society
Pages713-718
Number of pages6
ISBN (Print)9781479931064
DOIs
StatePublished - Jan 1 2014
Event11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 - Miami, FL, United States
Duration: Apr 7 2014Apr 9 2014

Publication series

NameProceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014

Other

Other11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
CountryUnited States
CityMiami, FL
Period4/7/144/9/14

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

Keywords

  • clustering
  • data mining
  • mussels wandering optimization
  • optimization
  • swarm intelligence

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