A spatial clustering algorithm for line objects based on extended Hausdorff distance

Author

Guiyun Zhou ; Baojia Lin ; Xiujun Ma

Author_Institution

Sch. of Resources & Environ., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

fYear

2013

fDate

20-22 June 2013

Firstpage

1

Lastpage

4

Abstract

Spatial clustering is one of the most commonly used approaches to spatial data mining. This study proposes an algorithm for clustering spatial line objects. Proximity between lines is measured using the extended Hausdorff distance. Lines are clustered using an improved K-means procedure. The procedure defines the kernel line of a cluster and the kernel lines of all clusters are updated in each iteration The algorithm is applied to tropical cyclone tracks of 20 years in the western Northern Pacific. Results show that the algorithm can partition lines to clusters that agree with human cognition.

Keywords

data mining; geophysics computing; pattern clustering; visual databases; extended Hausdorff distance; human cognition; improved k-means procedure; kernel lines; line objects; spatial clustering algorithm; spatial data mining; time 20 year; tropical cyclone tracks; western Northern Pacific; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Kernel; Partitioning algorithms; Tropical cyclones; Vectors; Hausdorff distance; K-means; spatial clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoinformatics (GEOINFORMATICS), 2013 21st International Conference on

Conference_Location

Kaifeng

ISSN

2161-024X

Type

conf

DOI

10.1109/Geoinformatics.2013.6626166

Filename

6626166