Forecast The Distribution Of Urban Water Point By Using Improved DBSCAN Algorithm

被引：2

作者：

Yan Jianzhuo ^{[1
]}

Qi Mengyao ^{[1
]}

Fang Liying ^{[1
]}

Wang Ying ^{[1
]}

Yu Jianyun ^{[2
]}

机构：

[1] Beijing Univ Technol, Elect Informat & Control Engn Inst, Beijing 100124, Peoples R China

[2] Capital Univ Econ & Busines, Educ & Technol Ctr, Beijing 100070, Peoples R China

来源：

2013 THIRD INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEM DESIGN AND ENGINEERING APPLICATIONS (ISDEA) | 2013年

关键词：

Spatial Data Mining; Density Clustering; DBSCAN Algorithm; Distribution of Urban Water Point; CLUSTERING TECHNIQUE; DATABASES;

D O I：

10.1109/ISDEA.2012.186

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Spatial clustering is an important method for spatial data mining and knowledge discovery. According to the deficiency existing in density-based clustering algorithm DBSCAN, such as the I/O overhead, memory consumption etc. This paper improves the DBSCAN algorithm, which proposed directional density algorithm, the algorithm reduces lots of points which need to be queried. By taking Geographic Information System for the application background, we successfully applied to forecast the distribution of urban water points. Compared with the traditional DBSCAN algorithm, the results conformed to the actual situation, and efficiency increased by 20%.

引用

页码：784 / 786

页数：3

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