Combining sampling technique with dbscan algorithm for clustering large spatial databases

Shuigeng Zhou; Aoying Zhou; Jing Cao; Jin Wen; Ye Fan; Yunfa Hu

doi:10.1007/3-540-45571-x_20

Combining sampling technique with dbscan algorithm for clustering large spatial databases

Shuigeng Zhou, Aoying Zhou, Jing Cao, Jin Wen, Ye Fan, Yunfa Hu

Fudan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Scopus citations

Abstract

In this paper, we combine sampUng technique with DBSCAN algorithm to cluster large spatial databases, two sampling-based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN; and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large-scale spatial databases.

Original language	English
Title of host publication	Knowledge Discovery and Data Mining
Subtitle of host publication	Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings
Editors	Takao Terano, Huan Liu, Arbee L.P. Chen
Publisher	Springer Verlag
Pages	169-172
Number of pages	4
ISBN (Print)	3540673822, 9783540673828
DOIs	https://doi.org/10.1007/3-540-45571-x_20
State	Published - 2000
Externally published	Yes
Event	4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 - Kyoto, Japan Duration: 18 Apr 2000 → 20 Apr 2000

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1805
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000
Country/Territory	Japan
City	Kyoto
Period	18/04/00 → 20/04/00

Access to Document

10.1007/3-540-45571-x_20

Cite this

Zhou, S., Zhou, A., Cao, J., Wen, J., Fan, Y., & Hu, Y. (2000). Combining sampling technique with dbscan algorithm for clustering large spatial databases. In T. Terano, H. Liu, & A. L. P. Chen (Eds.), Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings (pp. 169-172). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1805). Springer Verlag. https://doi.org/10.1007/3-540-45571-x_20

Zhou, Shuigeng ; Zhou, Aoying ; Cao, Jing et al. / Combining sampling technique with dbscan algorithm for clustering large spatial databases. Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. editor / Takao Terano ; Huan Liu ; Arbee L.P. Chen. Springer Verlag, 2000. pp. 169-172 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7590b5dcb171484cb50a33bc9e1a5ead,

title = "Combining sampling technique with dbscan algorithm for clustering large spatial databases",

abstract = "In this paper, we combine sampUng technique with DBSCAN algorithm to cluster large spatial databases, two sampling-based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN; and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large-scale spatial databases.",

author = "Shuigeng Zhou and Aoying Zhou and Jing Cao and Jin Wen and Ye Fan and Yunfa Hu",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2000.; 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 ; Conference date: 18-04-2000 Through 20-04-2000",

year = "2000",

doi = "10.1007/3-540-45571-x\_20",

language = "英语",

isbn = "3540673822",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "169--172",

editor = "Takao Terano and Huan Liu and Chen, \{Arbee L.P.\}",

booktitle = "Knowledge Discovery and Data Mining",

address = "德国",

}

Zhou, S, Zhou, A, Cao, J, Wen, J, Fan, Y & Hu, Y 2000, Combining sampling technique with dbscan algorithm for clustering large spatial databases. in T Terano, H Liu & ALP Chen (eds), Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1805, Springer Verlag, pp. 169-172, 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000, Kyoto, Japan, 18/04/00. https://doi.org/10.1007/3-540-45571-x_20

Combining sampling technique with dbscan algorithm for clustering large spatial databases. / Zhou, Shuigeng; Zhou, Aoying; Cao, Jing et al.
Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. ed. / Takao Terano; Huan Liu; Arbee L.P. Chen. Springer Verlag, 2000. p. 169-172 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1805).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Combining sampling technique with dbscan algorithm for clustering large spatial databases

AU - Zhou, Shuigeng

AU - Zhou, Aoying

AU - Cao, Jing

AU - Wen, Jin

AU - Fan, Ye

AU - Hu, Yunfa

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2000.

PY - 2000

Y1 - 2000

N2 - In this paper, we combine sampUng technique with DBSCAN algorithm to cluster large spatial databases, two sampling-based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN; and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large-scale spatial databases.

AB - In this paper, we combine sampUng technique with DBSCAN algorithm to cluster large spatial databases, two sampling-based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN; and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large-scale spatial databases.

UR - https://www.scopus.com/pages/publications/69349085438

U2 - 10.1007/3-540-45571-x_20

DO - 10.1007/3-540-45571-x_20

M3 - 会议稿件

AN - SCOPUS:69349085438

SN - 3540673822

SN - 9783540673828

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 169

EP - 172

BT - Knowledge Discovery and Data Mining

A2 - Terano, Takao

A2 - Liu, Huan

A2 - Chen, Arbee L.P.

PB - Springer Verlag

T2 - 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000

Y2 - 18 April 2000 through 20 April 2000

ER -

Zhou S, Zhou A, Cao J, Wen J, Fan Y, Hu Y. Combining sampling technique with dbscan algorithm for clustering large spatial databases. In Terano T, Liu H, Chen ALP, editors, Knowledge Discovery and Data Mining: Current Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings. Springer Verlag. 2000. p. 169-172. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45571-x_20

Combining sampling technique with dbscan algorithm for clustering large spatial databases

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