Cluster analysis by variance ratio criterion and quantum-behaved PSO

Shuihua Wang; Xingxing Zhou; Guangshuai Zhang; Genlin Ji; Jiquan Yang; Zheng Zhang; Zeyuan Lu; Yudong Zhang

doi:10.1007/978-3-319-27051-7_24

Cluster analysis by variance ratio criterion and quantum-behaved PSO

Shuihua Wang^*, Xingxing Zhou, Guangshuai Zhang, Genlin Ji, Jiquan Yang, Zheng Zhang, Zeyuan Lu, Yudong Zhang

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

(Aim) A novel and efficient method based on the quantum-behaved particle swarm was proposed to solve the cluster analysis problem. (Methods) The QPSO was utilized to detect the optimal point of the VAriance RAtio Criterion (VARAC), which was created by us as fitness function in the optimization model. The experimental dataset had 4 groups (400 data in total) with three various degrees of overlapping: non-overlapping, partial overlapping, and intensely overlapping. The proposed QPSO was compared with traditional global optimization algorithms: genetic algorithm (GA), combinatorial particle swarm optimization (CPSO), and firefly algorithm (FA) via running 20 times. (Results) The results demonstrated that QPSO could locate the best VARAC values with the least time among the four algorithms. (Conclusions) We can find that QPSO performs effectively and fast for the problem of cluster analysis.

Original language	English
Title of host publication	Cloud Computing and Security - 1st International Conference, ICCCS 2015, Revised Selected Papers
Editors	Xingming Sun, Junzhou Luo, Zhiqiu Huang, Jian Wang
Publisher	Springer Verlag
Pages	285-293
Number of pages	9
ISBN (Print)	9783319270500
DOIs	https://doi.org/10.1007/978-3-319-27051-7_24
Publication status	Published - 2015
Externally published	Yes
Event	1st International Conference on Cloud Computing and Security, ICCCS 2015 - Nanjing, China Duration: 13 Aug 2015 → 15 Aug 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9483
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Conference on Cloud Computing and Security, ICCCS 2015
Country/Territory	China
City	Nanjing
Period	13/08/15 → 15/08/15

Keywords

Cluster analysis
Particle swarm optimization (PSO)
Quantum-behaved PSO
VAriance RAtio Criterion (VARAC)

Access to Document

10.1007/978-3-319-27051-7_24

Cite this

Wang, S., Zhou, X., Zhang, G., Ji, G., Yang, J., Zhang, Z., Lu, Z., & Zhang, Y. (2015). Cluster analysis by variance ratio criterion and quantum-behaved PSO. In X. Sun, J. Luo, Z. Huang, & J. Wang (Eds.), Cloud Computing and Security - 1st International Conference, ICCCS 2015, Revised Selected Papers (pp. 285-293). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9483). Springer Verlag. https://doi.org/10.1007/978-3-319-27051-7_24

Wang, Shuihua ; Zhou, Xingxing ; Zhang, Guangshuai et al. / Cluster analysis by variance ratio criterion and quantum-behaved PSO. Cloud Computing and Security - 1st International Conference, ICCCS 2015, Revised Selected Papers. editor / Xingming Sun ; Junzhou Luo ; Zhiqiu Huang ; Jian Wang. Springer Verlag, 2015. pp. 285-293 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0359a4e11e6c4ad0be8d7451b27762dc,

title = "Cluster analysis by variance ratio criterion and quantum-behaved PSO",

abstract = "(Aim) A novel and efficient method based on the quantum-behaved particle swarm was proposed to solve the cluster analysis problem. (Methods) The QPSO was utilized to detect the optimal point of the VAriance RAtio Criterion (VARAC), which was created by us as fitness function in the optimization model. The experimental dataset had 4 groups (400 data in total) with three various degrees of overlapping: non-overlapping, partial overlapping, and intensely overlapping. The proposed QPSO was compared with traditional global optimization algorithms: genetic algorithm (GA), combinatorial particle swarm optimization (CPSO), and firefly algorithm (FA) via running 20 times. (Results) The results demonstrated that QPSO could locate the best VARAC values with the least time among the four algorithms. (Conclusions) We can find that QPSO performs effectively and fast for the problem of cluster analysis.",

keywords = "Cluster analysis, Particle swarm optimization (PSO), Quantum-behaved PSO, VAriance RAtio Criterion (VARAC)",

author = "Shuihua Wang and Xingxing Zhou and Guangshuai Zhang and Genlin Ji and Jiquan Yang and Zheng Zhang and Zeyuan Lu and Yudong Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 1st International Conference on Cloud Computing and Security, ICCCS 2015 ; Conference date: 13-08-2015 Through 15-08-2015",

year = "2015",

doi = "10.1007/978-3-319-27051-7_24",

language = "English",

isbn = "9783319270500",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "285--293",

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booktitle = "Cloud Computing and Security - 1st International Conference, ICCCS 2015, Revised Selected Papers",

}

Wang, S, Zhou, X, Zhang, G, Ji, G, Yang, J, Zhang, Z, Lu, Z & Zhang, Y 2015, Cluster analysis by variance ratio criterion and quantum-behaved PSO. in X Sun, J Luo, Z Huang & J Wang (eds), Cloud Computing and Security - 1st International Conference, ICCCS 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9483, Springer Verlag, pp. 285-293, 1st International Conference on Cloud Computing and Security, ICCCS 2015, Nanjing, China, 13/08/15. https://doi.org/10.1007/978-3-319-27051-7_24

Cluster analysis by variance ratio criterion and quantum-behaved PSO. / Wang, Shuihua; Zhou, Xingxing; Zhang, Guangshuai et al.
Cloud Computing and Security - 1st International Conference, ICCCS 2015, Revised Selected Papers. ed. / Xingming Sun; Junzhou Luo; Zhiqiu Huang; Jian Wang. Springer Verlag, 2015. p. 285-293 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9483).

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

TY - GEN

T1 - Cluster analysis by variance ratio criterion and quantum-behaved PSO

AU - Wang, Shuihua

AU - Zhou, Xingxing

AU - Zhang, Guangshuai

AU - Ji, Genlin

AU - Yang, Jiquan

AU - Zhang, Zheng

AU - Lu, Zeyuan

AU - Zhang, Yudong

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - (Aim) A novel and efficient method based on the quantum-behaved particle swarm was proposed to solve the cluster analysis problem. (Methods) The QPSO was utilized to detect the optimal point of the VAriance RAtio Criterion (VARAC), which was created by us as fitness function in the optimization model. The experimental dataset had 4 groups (400 data in total) with three various degrees of overlapping: non-overlapping, partial overlapping, and intensely overlapping. The proposed QPSO was compared with traditional global optimization algorithms: genetic algorithm (GA), combinatorial particle swarm optimization (CPSO), and firefly algorithm (FA) via running 20 times. (Results) The results demonstrated that QPSO could locate the best VARAC values with the least time among the four algorithms. (Conclusions) We can find that QPSO performs effectively and fast for the problem of cluster analysis.

AB - (Aim) A novel and efficient method based on the quantum-behaved particle swarm was proposed to solve the cluster analysis problem. (Methods) The QPSO was utilized to detect the optimal point of the VAriance RAtio Criterion (VARAC), which was created by us as fitness function in the optimization model. The experimental dataset had 4 groups (400 data in total) with three various degrees of overlapping: non-overlapping, partial overlapping, and intensely overlapping. The proposed QPSO was compared with traditional global optimization algorithms: genetic algorithm (GA), combinatorial particle swarm optimization (CPSO), and firefly algorithm (FA) via running 20 times. (Results) The results demonstrated that QPSO could locate the best VARAC values with the least time among the four algorithms. (Conclusions) We can find that QPSO performs effectively and fast for the problem of cluster analysis.

KW - Cluster analysis

KW - Particle swarm optimization (PSO)

KW - Quantum-behaved PSO

KW - VAriance RAtio Criterion (VARAC)

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U2 - 10.1007/978-3-319-27051-7_24

DO - 10.1007/978-3-319-27051-7_24

M3 - Conference Proceeding

AN - SCOPUS:84955247358

SN - 9783319270500

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 285

EP - 293

BT - Cloud Computing and Security - 1st International Conference, ICCCS 2015, Revised Selected Papers

A2 - Sun, Xingming

A2 - Luo, Junzhou

A2 - Huang, Zhiqiu

A2 - Wang, Jian

PB - Springer Verlag

T2 - 1st International Conference on Cloud Computing and Security, ICCCS 2015

Y2 - 13 August 2015 through 15 August 2015

ER -

Wang S, Zhou X, Zhang G, Ji G, Yang J, Zhang Z et al. Cluster analysis by variance ratio criterion and quantum-behaved PSO. In Sun X, Luo J, Huang Z, Wang J, editors, Cloud Computing and Security - 1st International Conference, ICCCS 2015, Revised Selected Papers. Springer Verlag. 2015. p. 285-293. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27051-7_24

Cluster analysis by variance ratio criterion and quantum-behaved PSO

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