JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.1 P.71-78

http://doi.org/10.1631/jzus.2005.A0071

A statistical information-based clustering approach in distance space

Author(s): Shi-hong Yue¹, Ping Li¹, Ji-dong Guo², Shui-geng Zhou¹
Affiliation(s): 1. Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China; more
Corresponding email(s): shyue@iipc.zju.edu.cn
Key Words: DBSCAN algorithm, Statistical information, Threshold

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YUE Shi-hong, LI Ping, GUO Ji-dong, ZHOU Shui-geng. A statistical information-based clustering approach in distance space[J]. Journal of Zhejiang University Science A, 2005, 6(1): 71-78.

@article{title="A statistical information-based clustering approach in distance space",
author="YUE Shi-hong, LI Ping, GUO Ji-dong, ZHOU Shui-geng",
journal="Journal of Zhejiang University Science A",
volume="6",
number="1",
pages="71-78",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0071"
}

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%T A statistical information-based clustering approach in distance space
%A YUE Shi-hong
%A LI Ping
%A GUO Ji-dong
%A ZHOU Shui-geng
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 1
%P 71-78
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0071

TY - JOUR
T1 - A statistical information-based clustering approach in distance space
A1 - YUE Shi-hong
A1 - LI Ping
A1 - GUO Ji-dong
A1 - ZHOU Shui-geng
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 1
SP - 71
EP - 78
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A0071

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Clustering, as a powerful data mining technique for discovering interesting data distributions and patterns in the underlying database, is used in many fields, such as statistical data analysis, pattern recognition, image processing, and other business applications. Density-based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996) is a good performance clustering method for dealing with spatial data although it leaves many problems to be solved. For example, DBSCAN requires a necessary user-specified threshold while its computation is extremely time-consuming by current method such as OPTICS, etc. (Ankerst et al., 1999), and the performance of DBSCAN under different norms has yet to be examined. In this paper, we first developed a method based on statistical information of distance space in database to determine the necessary threshold. Then our examination of the DBSCAN performance under different norms showed that there was determinable relation between them. Finally, we used two artificial databases to verify the effectiveness and efficiency of the proposed methods.

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References

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