L-nearest neighbors ant colony optimization for data clustering

Shih Pang Tseng, Ming Chao Chiang, Chu Sing Yang

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

1 Citation (Scopus)

Abstract

It is an important trend to apply the metaheuristics, such as ant colony optimization (ACO), to data clustering. In general, the ACO for data clustering can accomplish better quality of clustering. In this paper, we proposed an improved ACO, to enhance the efficiency of ACO for data clustering. It is based on the assumption that there are at least one or more neighbors belong to the same cluster in the L nearest neighbors of each instance. It modifies the operation of constructing solution to reduce the computation time of Euclidean distance. The experimental results show that the L-NNACO is faster than ACO about 38% to 54%. In addition, the L-NNACO is with greater or equal accuracy to the ACO for the various datasets of real world.

Original languageEnglish
Title of host publicationProceedings - International Conference on Machine Learning and Cybernetics
PublisherIEEE Computer Society
Pages1684-1690
Number of pages7
ISBN (Electronic)9781479902576
DOIs
Publication statusPublished - 2013
Event12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China
Duration: 2013 Jul 142013 Jul 17

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume4
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Other

Other12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
CountryChina
CityTianjin
Period13-07-1413-07-17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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