Forecast the Distribution of Urban Water Point by Using Improved DBSCAN Algorithm

Author

Yan Jianzhuo ; Qi Mengyao ; Fang Liying ; Wang Ying ; Yu Jianyun

Author_Institution

Electron. Inf. & control Eng. Inst., Beijing Univ. of Technol., Beijing, China

fYear

2013

fDate

16-18 Jan. 2013

Firstpage

784

Lastpage

786

Abstract

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%.

Keywords

data mining; forecasting theory; geographic information systems; pattern clustering; water supply; DBSCAN algorithm; I/O overhead; application background; density-based clustering algorithm; directional density algorithm; geographic information system; knowledge discovery; memory consumption; spatial clustering; spatial data mining; urban water point distribution forecasting; Algorithm design and analysis; Clustering algorithms; Data mining; Information systems; Spatial databases; Vectors; Water; DBSCAN Algorithm; Density Clustering; Distribution of Urban Water Point; Spatial Data Mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent System Design and Engineering Applications (ISDEA), 2013 Third International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4673-4893-5

Type

conf

DOI

10.1109/ISDEA.2012.186

Filename

6455819