基于粒子群算法的障碍物约束空间数据聚类

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI:10.1109/FSKD.2007.219

Xueping Zhang, Fen Qin, Jiayao Wang, Yongheng Fu, Jinghui Chen

{"title":"基于粒子群算法的障碍物约束空间数据聚类","authors":"Xueping Zhang, Fen Qin, Jiayao Wang, Yongheng Fu, Jinghui Chen","doi":"10.1109/FSKD.2007.219","DOIUrl":null,"url":null,"abstract":"This paper proposes a particle swarm optimization (PSO) method for solving Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use PSO to get obstructed distance, and then we developed the PSO K-Medoids SCOC (PKSCOC) to cluster spatial data with obstacles constraints. The experimental results show that PKSCOC performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Clustering Spatial Data with Obstacles Constraints by PSO\",\"authors\":\"Xueping Zhang, Fen Qin, Jiayao Wang, Yongheng Fu, Jinghui Chen\",\"doi\":\"10.1109/FSKD.2007.219\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a particle swarm optimization (PSO) method for solving Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use PSO to get obstructed distance, and then we developed the PSO K-Medoids SCOC (PKSCOC) to cluster spatial data with obstacles constraints. The experimental results show that PKSCOC performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).\",\"PeriodicalId\":201883,\"journal\":{\"name\":\"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2007.219\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2007.219","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

提出了一种基于粒子群算法的障碍物约束空间聚类问题求解方法。在此过程中，我们首先使用PSO来获取障碍物距离，然后我们开发了PSO K-Medoids SCOC (PKSCOC)来对具有障碍物约束的空间数据进行聚类。实验结果表明，PKSCOC在量化误差方面优于改进K-Medoids SCOC (IKSCOC)，在收敛速度方面优于遗传K-Medoids SCOC (GKSCOC)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Clustering Spatial Data with Obstacles Constraints by PSO

This paper proposes a particle swarm optimization (PSO) method for solving Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use PSO to get obstructed distance, and then we developed the PSO K-Medoids SCOC (PKSCOC) to cluster spatial data with obstacles constraints. The experimental results show that PKSCOC performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

自引率

0.00%

发文量