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

^*此作品的通讯作者

网络空间安全学院

Henan University of Technology
Information Engineering University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

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.

源语言	英语
主期刊名	Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
页	254-261
页数	8
版本	PART 1
DOI	https://doi.org/10.1007/978-3-642-13657-3_29
出版状态	已出版 - 2010
活动	14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, 印度期限: 21 6月 2010 → 24 6月 2010

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
编号	PART 1
卷	6118 LNAI
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
国家/地区	印度
市	Hyderabad
时期	21/06/10 → 24/06/10

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, X., Du, H., & Wang, J. (2010). Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO. 在 Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings (PART 1 编辑, 页码 254-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 6118 LNAI, 号码 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. 编辑 2010. 页码 254-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{3854824a1155485c9b02d0c8b8339cd0,

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",

year = "2010",

doi = "10.1007/978-3-642-13657-3\_29",

language = "英语",

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

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

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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. 在 Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1 编辑, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 号码 PART 1, 卷 6118 LNAI, 页码 254-261, 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010, Hyderabad, 印度, 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. 编辑 2010. 页码 254-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 6118 LNAI, 号码 PART 1).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO

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

UR - https://www.scopus.com/pages/publications/79956307128

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

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

M3 - 会议稿件

AN - SCOPUS:79956307128

SN - 3642136567

SN - 9783642136566

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

SP - 254

EP - 261

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

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

Y2 - 21 June 2010 through 24 June 2010

ER -

Zhang X, Du H, Wang J. Spatial Clustering with Obstacles Constraints by dynamic piecewise-mapped and nonlinear inertia weights PSO. 在 Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1 编辑 2010. 页码 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

摘要

出版系列

会议

访问文件

其它文件与链接

指纹

引用此