GMDBSCAN: Multi-Density DBSCAN Cluster Based on Grid

Author

Xiaoyun, Chen ; Yufang, Min ; Yan, Zhao ; Ping, Wang

Author_Institution

Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou

fYear

2008

Firstpage

780

Lastpage

783

Abstract

DBSCAN is one of the most popular algorithms for cluster analysis. It can discover all clusters with arbitrary shape and separate noises. But this algorithm canpsilat choose parameter according to distributing of dataset. It simply uses the global MinPts parameter, so that the clustering result of multi-density database is inaccurate. In addition, when it is used to cluster large databases, it will cost too much time. For these problems, we propose GMDBSCAN algorithm which is based on spatial index and grid technique. An experimental evaluation shows that GMDBSCAN is effective and efficient.

Keywords

data mining; grid computing; pattern clustering; very large databases; GMDBSCAN; cluster analysis; global MinPts parameter; grid technique; large databases; multidensity DBSCAN cluster; multidensity database; spatial index; Algorithm design and analysis; Clustering algorithms; Costs; Information analysis; Information science; Multi-stage noise shaping; Partial response channels; Shape; Spatial databases; Spatial indexes; Clustering; Data Mining; Local_MinPts; Multi-Density; SP-Tree; Unit Grid Density;

fLanguage

English

Publisher

ieee

Conference_Titel

e-Business Engineering, 2008. ICEBE '08. IEEE International Conference on

Conference_Location

Xi´an

Print_ISBN

978-0-7695-3395-7

Type

conf

DOI

10.1109/ICEBE.2008.54

Filename

4690704