L-nearest neighbors ant colony optimization for data clustering

Shih Pang Tseng; Ming Chao Chiang; Chu Sing Yang

doi:10.1109/ICMLC.2013.6890869

L-nearest neighbors ant colony optimization for data clustering

Shih Pang Tseng, Ming Chao Chiang, Chu Sing Yang

MS Degree in AI Robotics

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings - International Conference on Machine Learning and Cybernetics
Publisher	IEEE Computer Society
Pages	1684-1690
Number of pages	7
ISBN (Electronic)	9781479902576
DOIs	https://doi.org/10.1109/ICMLC.2013.6890869
Publication status	Published - 2013
Event	12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China Duration: 2013 Jul 14 → 2013 Jul 17

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	4
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Other

Other	12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
Country/Territory	China
City	Tianjin
Period	13-07-14 → 13-07-17

All Science Journal Classification (ASJC) codes

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

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10.1109/ICMLC.2013.6890869

Cite this

Tseng, S. P., Chiang, M. C., & Yang, C. S. (2013). L-nearest neighbors ant colony optimization for data clustering. In Proceedings - International Conference on Machine Learning and Cybernetics (pp. 1684-1690). Article 6890869 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 4). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2013.6890869

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title = "L-nearest neighbors ant colony optimization for data clustering",

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.",

author = "Tseng, {Shih Pang} and Chiang, {Ming Chao} and Yang, {Chu Sing}",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.; 12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 ; Conference date: 14-07-2013 Through 17-07-2013",

year = "2013",

doi = "10.1109/ICMLC.2013.6890869",

language = "English",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

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Tseng, SP, Chiang, MC & Yang, CS 2013, L-nearest neighbors ant colony optimization for data clustering. in Proceedings - International Conference on Machine Learning and Cybernetics., 6890869, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 4, IEEE Computer Society, pp. 1684-1690, 12th International Conference on Machine Learning and Cybernetics, ICMLC 2013, Tianjin, China, 13-07-14. https://doi.org/10.1109/ICMLC.2013.6890869

L-nearest neighbors ant colony optimization for data clustering. / Tseng, Shih Pang; Chiang, Ming Chao; Yang, Chu Sing.
Proceedings - International Conference on Machine Learning and Cybernetics. IEEE Computer Society, 2013. p. 1684-1690 6890869 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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L-nearest neighbors ant colony optimization for data clustering

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