Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO

Xueping Zhang; Haohua Du; Jiayao Wang

doi:10.1007/978-3-642-13657-3_29

Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO

Xueping Zhang^*
, Haohua Du
, Jiayao Wang

^*Corresponding author for this work

School of Cyber Science and Technology

Henan University of Technology
Information Engineering University

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

Abstract

Spatial clustering with constraints has been a new topic in spatial data mining. A novel Spatial Clustering with Obstacles Constraints (SCOC) by dynamic piecewise-mapped and nonlinear inertia weights particle swarm optimization is proposed in this paper. The experiments show that the algorithm 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 PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic KMedoids SCOC.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
Pages	254-261
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-13657-3_29
State	Published - 2010
Event	14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, India Duration: 21 Jun 2010 → 24 Jun 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6118 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
Country/Territory	India
City	Hyderabad
Period	21/06/10 → 24/06/10

Keywords

Dynamic nonlinear inertia weights
Dynamic piecewise linear chaotic map
Particle swarm optimization
Spatial clustering with obstacles constraints

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10.1007/978-3-642-13657-3_29

Cite this

Zhang, X., Du, H., & Wang, J. (2010). Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO. In Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings (PART 1 ed., pp. 254-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6118 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-13657-3_29

Zhang, Xueping ; Du, Haohua ; Wang, Jiayao. / Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO. Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1. ed. 2010. pp. 254-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO",

abstract = "Spatial clustering with constraints has been a new topic in spatial data mining. A novel Spatial Clustering with Obstacles Constraints (SCOC) by dynamic piecewise-mapped and nonlinear inertia weights particle swarm optimization is proposed in this paper. The experiments show that the algorithm 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 PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic KMedoids SCOC.",

keywords = "Dynamic nonlinear inertia weights, Dynamic piecewise linear chaotic map, Particle swarm optimization, Spatial clustering with obstacles constraints",

author = "Xueping Zhang and Haohua Du and Jiayao Wang",

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booktitle = "Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings",

edition = "PART 1",

note = "14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 ; Conference date: 21-06-2010 Through 24-06-2010",

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Zhang, X, Du, H & Wang, J 2010, Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO. in Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6118 LNAI, pp. 254-261, 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010, Hyderabad, India, 21/06/10. https://doi.org/10.1007/978-3-642-13657-3_29

Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO. / Zhang, Xueping; Du, Haohua; Wang, Jiayao.
Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1. ed. 2010. p. 254-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6118 LNAI, No. PART 1).

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

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AU - Zhang, Xueping

AU - Du, Haohua

AU - Wang, Jiayao

PY - 2010

Y1 - 2010

N2 - Spatial clustering with constraints has been a new topic in spatial data mining. A novel Spatial Clustering with Obstacles Constraints (SCOC) by dynamic piecewise-mapped and nonlinear inertia weights particle swarm optimization is proposed in this paper. The experiments show that the algorithm 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 PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic KMedoids SCOC.

AB - Spatial clustering with constraints has been a new topic in spatial data mining. A novel Spatial Clustering with Obstacles Constraints (SCOC) by dynamic piecewise-mapped and nonlinear inertia weights particle swarm optimization is proposed in this paper. The experiments show that the algorithm 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 PSO K-Medoids SCOC in terms of quantization error and has higher constringency speed than Genetic KMedoids SCOC.

KW - Dynamic nonlinear inertia weights

KW - Dynamic piecewise linear chaotic map

KW - Particle swarm optimization

KW - Spatial clustering with obstacles constraints

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U2 - 10.1007/978-3-642-13657-3_29

DO - 10.1007/978-3-642-13657-3_29

M3 - 会议稿件

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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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T2 - 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010

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Zhang X, Du H, Wang J. Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO. In Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1 ed. 2010. p. 254-261. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-13657-3_29

Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO

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