L-nearest neighbors ant colony optimization for data clustering

Shih Pang Tseng, Ming Chao Chiang, Chu Sing Yang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - International Conference on Machine Learning and Cybernetics
發行者IEEE Computer Society
頁面1684-1690
頁數7
ISBN(電子)9781479902576
DOIs
出版狀態Published - 2013
事件12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China
持續時間: 2013 七月 142013 七月 17

出版系列

名字Proceedings - International Conference on Machine Learning and Cybernetics
4
ISSN(列印)2160-133X
ISSN(電子)2160-1348

Other

Other12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
國家/地區China
城市Tianjin
期間13-07-1413-07-17

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 計算機理論與數學
  • 電腦網路與通信
  • 人機介面

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