A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids

Xueping Zhang; Haohua Du; Tengfei Yang; Guangcai Zhao

doi:10.1007/978-3-642-13498-2_62

A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids

Xueping Zhang^*
, Haohua Du
, Tengfei Yang
, Guangcai Zhao

^*Corresponding author for this work

School of Cyber Science and Technology

Henan University of Technology

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

1 Scopus citations

Abstract

In this paper, we propose a novel Spatial Clustering with Obstacles Constraints (SCOC) based on Dynamic Piecewise Linear Chaotic Map and Dynamic Nonlinear Particle Swarm Optimization (PNPSO) and K-Medoids, which is called PNPKSCOC. The contrastive experiments show that PNPKSCOC is effective and has better practicalities, and it performs better than PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.

Original language	English
Title of host publication	Advances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings
Pages	476-483
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-13498-2_62
State	Published - 2010
Event	1st International Conference on Advances in Swarm Intelligence, ICSI 2010 - Beijing, China Duration: 12 Jun 2010 → 15 Jun 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6146 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Conference on Advances in Swarm Intelligence, ICSI 2010
Country/Territory	China
City	Beijing
Period	12/06/10 → 15/06/10

Keywords

K-Medoids
Nonlinear Inertia Weights
Obstacles Constraints
Particle Swarm Optimization
Piecewise Linear Chaotic Map
Spatial Clustering

Access to Document

10.1007/978-3-642-13498-2_62

Cite this

Zhang, X., Du, H., Yang, T., & Zhao, G. (2010). A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids. In Advances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings (PART 2 ed., pp. 476-483). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6146 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-13498-2_62

Zhang, Xueping ; Du, Haohua ; Yang, Tengfei et al. / A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids. Advances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings. PART 2. ed. 2010. pp. 476-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{dc26f3d640d54fadafb1bab0ad973e0b,

title = "A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids",

abstract = "In this paper, we propose a novel Spatial Clustering with Obstacles Constraints (SCOC) based on Dynamic Piecewise Linear Chaotic Map and Dynamic Nonlinear Particle Swarm Optimization (PNPSO) and K-Medoids, which is called PNPKSCOC. The contrastive experiments show that PNPKSCOC is effective and has better practicalities, and it performs better than PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.",

keywords = "K-Medoids, Nonlinear Inertia Weights, Obstacles Constraints, Particle Swarm Optimization, Piecewise Linear Chaotic Map, Spatial Clustering",

author = "Xueping Zhang and Haohua Du and Tengfei Yang and Guangcai Zhao",

year = "2010",

doi = "10.1007/978-3-642-13498-2\_62",

language = "英语",

isbn = "3642134971",

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

number = "PART 2",

pages = "476--483",

booktitle = "Advances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings",

edition = "PART 2",

note = "1st International Conference on Advances in Swarm Intelligence, ICSI 2010 ; Conference date: 12-06-2010 Through 15-06-2010",

}

Zhang, X, Du, H, Yang, T & Zhao, G 2010, A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids. in Advances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6146 LNCS, pp. 476-483, 1st International Conference on Advances in Swarm Intelligence, ICSI 2010, Beijing, China, 12/06/10. https://doi.org/10.1007/978-3-642-13498-2_62

A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids. / Zhang, Xueping; Du, Haohua; Yang, Tengfei et al.
Advances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings. PART 2. ed. 2010. p. 476-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6146 LNCS, No. PART 2).

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

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N2 - In this paper, we propose a novel Spatial Clustering with Obstacles Constraints (SCOC) based on Dynamic Piecewise Linear Chaotic Map and Dynamic Nonlinear Particle Swarm Optimization (PNPSO) and K-Medoids, which is called PNPKSCOC. The contrastive experiments show that PNPKSCOC is effective and has better practicalities, and it performs better than PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.

AB - In this paper, we propose a novel Spatial Clustering with Obstacles Constraints (SCOC) based on Dynamic Piecewise Linear Chaotic Map and Dynamic Nonlinear Particle Swarm Optimization (PNPSO) and K-Medoids, which is called PNPKSCOC. The contrastive experiments show that PNPKSCOC is effective and has better practicalities, and it performs better than PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC.

KW - K-Medoids

KW - Nonlinear Inertia Weights

KW - Obstacles Constraints

KW - Particle Swarm Optimization

KW - Piecewise Linear Chaotic Map

KW - Spatial Clustering

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

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Zhang X, Du H, Yang T, Zhao G. A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids. In Advances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings. PART 2 ed. 2010. p. 476-483. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-13498-2_62

A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids

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Keywords

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