A hybrid approach to clustering in very large databases

Aoying Zhou; Weining Qian; Hailei Qian; Jin Wen; Shuigeng Zhou; Ye Fan

doi:10.1007/3-540-45357-1_55

A hybrid approach to clustering in very large databases

Aoying Zhou
, Weining Qian
, Hailei Qian
, Jin Wen
, Shuigeng Zhou
, Ye Fan

Fudan University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Current clustering methods always have such problems: 1) High I/O cost and expensive maintenance; 2) Pre-specifying the uncertain parameter k; 3) Lacking good efficiency in treating arbitrary shape under very large data set environment. In this paper, we first present a hybrid-clustering algorithm to solve these problems. It combines both distance and density strategies, and makes full use of statistics information while keeping good cluster quality. The experimental results show that our algorithm outperforms other popular algorithms in terms of efficiency, cost, and even get much more speedup as the data size scales up.

源语言	英语
主期刊名	Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings
编辑	David Cheung, Graham J. Williams, Qing Li
出版商	Springer Verlag
页	519-524
页数	6
ISBN（印刷版）	3540419101, 9783540419105
DOI	https://doi.org/10.1007/3-540-45357-1_55
出版状态	已出版 - 2001
已对外发布	是
活动	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 - Kowloon, 香港期限: 16 4月 2001 → 18 4月 2001

出版系列

姓名	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
卷	2035
ISSN（印刷版）	0302-9743

会议

会议	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001
国家/地区	香港
市	Kowloon
时期	16/04/01 → 18/04/01

访问文件

10.1007/3-540-45357-1_55

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhou, A., Qian, W., Qian, H., Wen, J., Zhou, S., & Fan, Y. (2001). A hybrid approach to clustering in very large databases. 在 D. Cheung, G. J. Williams, & Q. Li (编辑), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings (页码 519-524). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); 卷 2035). Springer Verlag. https://doi.org/10.1007/3-540-45357-1_55

Zhou, Aoying ; Qian, Weining ; Qian, Hailei 等. / A hybrid approach to clustering in very large databases. Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. 编辑 / David Cheung ; Graham J. Williams ; Qing Li. Springer Verlag, 2001. 页码 519-524 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).

@inproceedings{f88ddd2adaa44077b58912376222688f,

title = "A hybrid approach to clustering in very large databases",

abstract = "Current clustering methods always have such problems: 1) High I/O cost and expensive maintenance; 2) Pre-specifying the uncertain parameter k; 3) Lacking good efficiency in treating arbitrary shape under very large data set environment. In this paper, we first present a hybrid-clustering algorithm to solve these problems. It combines both distance and density strategies, and makes full use of statistics information while keeping good cluster quality. The experimental results show that our algorithm outperforms other popular algorithms in terms of efficiency, cost, and even get much more speedup as the data size scales up.",

author = "Aoying Zhou and Weining Qian and Hailei Qian and Jin Wen and Shuigeng Zhou and Ye Fan",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 ; Conference date: 16-04-2001 Through 18-04-2001",

year = "2001",

doi = "10.1007/3-540-45357-1\_55",

language = "英语",

isbn = "3540419101",

series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

publisher = "Springer Verlag",

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}

Zhou, A , Qian, W, Qian, H, Wen, J, Zhou, S & Fan, Y 2001, A hybrid approach to clustering in very large databases. 在 D Cheung, GJ Williams & Q Li (编辑), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 卷 2035, Springer Verlag, 页码 519-524, 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, Kowloon, 香港, 16/04/01. https://doi.org/10.1007/3-540-45357-1_55

A hybrid approach to clustering in very large databases. / Zhou, Aoying ; Qian, Weining; Qian, Hailei 等.
Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. 编辑 / David Cheung; Graham J. Williams; Qing Li. Springer Verlag, 2001. 页码 519-524 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); 卷 2035).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - A hybrid approach to clustering in very large databases

AU - Zhou, Aoying

AU - Qian, Weining

AU - Qian, Hailei

AU - Wen, Jin

AU - Zhou, Shuigeng

AU - Fan, Ye

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2001.

PY - 2001

Y1 - 2001

N2 - Current clustering methods always have such problems: 1) High I/O cost and expensive maintenance; 2) Pre-specifying the uncertain parameter k; 3) Lacking good efficiency in treating arbitrary shape under very large data set environment. In this paper, we first present a hybrid-clustering algorithm to solve these problems. It combines both distance and density strategies, and makes full use of statistics information while keeping good cluster quality. The experimental results show that our algorithm outperforms other popular algorithms in terms of efficiency, cost, and even get much more speedup as the data size scales up.

AB - Current clustering methods always have such problems: 1) High I/O cost and expensive maintenance; 2) Pre-specifying the uncertain parameter k; 3) Lacking good efficiency in treating arbitrary shape under very large data set environment. In this paper, we first present a hybrid-clustering algorithm to solve these problems. It combines both distance and density strategies, and makes full use of statistics information while keeping good cluster quality. The experimental results show that our algorithm outperforms other popular algorithms in terms of efficiency, cost, and even get much more speedup as the data size scales up.

UR - https://www.scopus.com/pages/publications/84942933944

U2 - 10.1007/3-540-45357-1_55

DO - 10.1007/3-540-45357-1_55

M3 - 会议稿件

AN - SCOPUS:84942933944

SN - 3540419101

SN - 9783540419105

T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

SP - 519

EP - 524

BT - Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings

A2 - Cheung, David

A2 - Williams, Graham J.

A2 - Li, Qing

PB - Springer Verlag

T2 - 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001

Y2 - 16 April 2001 through 18 April 2001

ER -

Zhou A , Qian W, Qian H, Wen J, Zhou S, Fan Y. A hybrid approach to clustering in very large databases. 在 Cheung D, Williams GJ, Li Q, 编辑, Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. Springer Verlag. 2001. 页码 519-524. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)). doi: 10.1007/3-540-45357-1_55

A hybrid approach to clustering in very large databases

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