A quantum particle swarm optimization used for spatial clustering with obstacles constraints

Xueping Zhang; Jiayao Wang; Haohua Du; Tengfei Yang; Yawei Liu

doi:10.1007/978-3-642-04020-7_45

A quantum particle swarm optimization used for spatial clustering with obstacles constraints

Xueping Zhang^*
, Jiayao Wang
, Haohua Du
, Tengfei Yang
, Yawei Liu

^*Corresponding author for this work

School of Cyber Science and Technology

Henan University of Technology
Fuzhou University
Information Engineering University

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

2 Scopus citations

Abstract

In this paper, a more effective Quantum Particle Swarm Optimization (QPSO) method for Spatial Clustering with Obstacles Constraints (SCOC) is presented. In the process of doing so, we first proposed a novel Spatial Obstructed Distance using QPSO based on Grid model (QPGSOD) to obtain obstructed distance, and then we developed a new QPKSCOC based on QPSO and K-Medoids to cluster spatial data with obstacles constraints. The contrastive experiments show that QPGSOD is effective, and QPKSCOC can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering; and it performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.

Original language	English
Title of host publication	Emerging Intelligent Computing Technology and Applications
Subtitle of host publication	With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings
Publisher	Springer Verlag
Pages	424-433
Number of pages	10
ISBN (Print)	3642040195, 9783642040191
DOIs	https://doi.org/10.1007/978-3-642-04020-7_45
State	Published - 2009
Event	5th International Conference on Intelligent Computing, ICIC 2009 - Ulsan, Korea, Republic of Duration: 16 Sep 2009 → 19 Sep 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5755 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Intelligent Computing, ICIC 2009
Country/Territory	Korea, Republic of
City	Ulsan
Period	16/09/09 → 19/09/09

Keywords

Obstacles constraints
Quantum particle swarm optimization
Spatial clustering
Spatial obstructed distance

Access to Document

10.1007/978-3-642-04020-7_45

Cite this

Zhang, X., Wang, J., Du, H., Yang, T., & Liu, Y. (2009). A quantum particle swarm optimization used for spatial clustering with obstacles constraints. In Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings (pp. 424-433). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5755 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-642-04020-7_45

Zhang, Xueping ; Wang, Jiayao ; Du, Haohua et al. / A quantum particle swarm optimization used for spatial clustering with obstacles constraints. Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings. Springer Verlag, 2009. pp. 424-433 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A quantum particle swarm optimization used for spatial clustering with obstacles constraints",

abstract = "In this paper, a more effective Quantum Particle Swarm Optimization (QPSO) method for Spatial Clustering with Obstacles Constraints (SCOC) is presented. In the process of doing so, we first proposed a novel Spatial Obstructed Distance using QPSO based on Grid model (QPGSOD) to obtain obstructed distance, and then we developed a new QPKSCOC based on QPSO and K-Medoids to cluster spatial data with obstacles constraints. The contrastive experiments show that QPGSOD is effective, and QPKSCOC can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering; and it performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.",

keywords = "Obstacles constraints, Quantum particle swarm optimization, Spatial clustering, Spatial obstructed distance",

author = "Xueping Zhang and Jiayao Wang and Haohua Du and Tengfei Yang and Yawei Liu",

year = "2009",

doi = "10.1007/978-3-642-04020-7\_45",

language = "英语",

isbn = "3642040195",

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

publisher = "Springer Verlag",

pages = "424--433",

booktitle = "Emerging Intelligent Computing Technology and Applications",

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note = "5th International Conference on Intelligent Computing, ICIC 2009 ; Conference date: 16-09-2009 Through 19-09-2009",

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Zhang, X, Wang, J, Du, H, Yang, T & Liu, Y 2009, A quantum particle swarm optimization used for spatial clustering with obstacles constraints. in Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5755 LNAI, Springer Verlag, pp. 424-433, 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, Korea, Republic of, 16/09/09. https://doi.org/10.1007/978-3-642-04020-7_45

A quantum particle swarm optimization used for spatial clustering with obstacles constraints. / Zhang, Xueping; Wang, Jiayao; Du, Haohua et al.
Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings. Springer Verlag, 2009. p. 424-433 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5755 LNAI).

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

TY - GEN

T1 - A quantum particle swarm optimization used for spatial clustering with obstacles constraints

AU - Zhang, Xueping

AU - Wang, Jiayao

AU - Du, Haohua

AU - Yang, Tengfei

AU - Liu, Yawei

PY - 2009

Y1 - 2009

N2 - In this paper, a more effective Quantum Particle Swarm Optimization (QPSO) method for Spatial Clustering with Obstacles Constraints (SCOC) is presented. In the process of doing so, we first proposed a novel Spatial Obstructed Distance using QPSO based on Grid model (QPGSOD) to obtain obstructed distance, and then we developed a new QPKSCOC based on QPSO and K-Medoids to cluster spatial data with obstacles constraints. The contrastive experiments show that QPGSOD is effective, and QPKSCOC can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering; and it performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.

AB - In this paper, a more effective Quantum Particle Swarm Optimization (QPSO) method for Spatial Clustering with Obstacles Constraints (SCOC) is presented. In the process of doing so, we first proposed a novel Spatial Obstructed Distance using QPSO based on Grid model (QPGSOD) to obtain obstructed distance, and then we developed a new QPKSCOC based on QPSO and K-Medoids to cluster spatial data with obstacles constraints. The contrastive experiments show that QPGSOD is effective, and QPKSCOC can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering; and it performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.

KW - Obstacles constraints

KW - Quantum particle swarm optimization

KW - Spatial clustering

KW - Spatial obstructed distance

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DO - 10.1007/978-3-642-04020-7_45

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SN - 3642040195

SN - 9783642040191

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

SP - 424

EP - 433

BT - Emerging Intelligent Computing Technology and Applications

PB - Springer Verlag

T2 - 5th International Conference on Intelligent Computing, ICIC 2009

Y2 - 16 September 2009 through 19 September 2009

ER -

Zhang X, Wang J, Du H, Yang T, Liu Y. A quantum particle swarm optimization used for spatial clustering with obstacles constraints. In Emerging Intelligent Computing Technology and Applications: With Aspects of Artificial Intelligence - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings. Springer Verlag. 2009. p. 424-433. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04020-7_45

A quantum particle swarm optimization used for spatial clustering with obstacles constraints

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Keywords

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