A4C: An adaptive artificial ants clustering algorithm

Xiaohua Xu; Ling Chen; Yixin Chen

A⁴C: An adaptive artificial ants clustering algorithm

Xiaohua Xu
, Ling Chen
, Yixin Chen

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

Abstract

With the advance of microarray technology, clustering analysis has become a key tool to make sense of the massive amounts of genes expression data. In this paper, an artificial Ants Sleeping Model (ASM) and an Adaptive Artificial Ants Clustering Algorithm (A⁴C) are presented to solve the clustering problem in data mining by simulating the behaviors of social ant colonies. In the ASM model, each datum is represented by an agent. The agents' environment is a two-dimensional grid. In A⁴C, the agents can form into high-quality clusters by making simple moves according to little local information from its neighborhood and the parameters are selected and adjusted adaptively. Experimental results on clustering benchmarks show the ASM and A⁴C are simpler, easier to implement, and more efficient than previous methods.

Original language	English
Title of host publication	Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04
Pages	268-275
Number of pages	8
State	Published - 2004
Event	Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04 - La Jolla, CA, United States Duration: Oct 7 2004 → Oct 8 2004

Publication series

Name	Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04

Conference

Conference	Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04
Country/Territory	United States
City	La Jolla, CA
Period	10/7/04 → 10/8/04

Keywords

Ant colony algorithm
Data mining
Self-organization
Swarm intelligence

Cite this

@inproceedings{68961babefb5407c8bb66e3dc92d8dce,

title = "A4C: An adaptive artificial ants clustering algorithm",

abstract = "With the advance of microarray technology, clustering analysis has become a key tool to make sense of the massive amounts of genes expression data. In this paper, an artificial Ants Sleeping Model (ASM) and an Adaptive Artificial Ants Clustering Algorithm (A4C) are presented to solve the clustering problem in data mining by simulating the behaviors of social ant colonies. In the ASM model, each datum is represented by an agent. The agents' environment is a two-dimensional grid. In A4C, the agents can form into high-quality clusters by making simple moves according to little local information from its neighborhood and the parameters are selected and adjusted adaptively. Experimental results on clustering benchmarks show the ASM and A4C are simpler, easier to implement, and more efficient than previous methods.",

keywords = "Ant colony algorithm, Data mining, Self-organization, Swarm intelligence",

author = "Xiaohua Xu and Ling Chen and Yixin Chen",

year = "2004",

language = "English",

isbn = "0780387287",

series = "Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04",

pages = "268--275",

booktitle = "Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04",

note = "Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04 ; Conference date: 07-10-2004 Through 08-10-2004",

}

Xu, X, Chen, L & Chen, Y 2004, A⁴C: An adaptive artificial ants clustering algorithm. in Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04. Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04, pp. 268-275, Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04, La Jolla, CA, United States, 10/7/04.

A⁴C: An adaptive artificial ants clustering algorithm. / Xu, Xiaohua; Chen, Ling; Chen, Yixin.
Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04. 2004. p. 268-275 (Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04).

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

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AB - With the advance of microarray technology, clustering analysis has become a key tool to make sense of the massive amounts of genes expression data. In this paper, an artificial Ants Sleeping Model (ASM) and an Adaptive Artificial Ants Clustering Algorithm (A4C) are presented to solve the clustering problem in data mining by simulating the behaviors of social ant colonies. In the ASM model, each datum is represented by an agent. The agents' environment is a two-dimensional grid. In A4C, the agents can form into high-quality clusters by making simple moves according to little local information from its neighborhood and the parameters are selected and adjusted adaptively. Experimental results on clustering benchmarks show the ASM and A4C are simpler, easier to implement, and more efficient than previous methods.

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KW - Data mining

KW - Self-organization

KW - Swarm intelligence

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