An efficient algorithm for dense regions discovery from large-scale data streams

Andy M. Yip; Edmond H. Wu; Michael K. Ng; Tony F. Chan

doi:10.1007/978-3-540-24775-3_14

An efficient algorithm for dense regions discovery from large-scale data streams

Andy M. Yip, Edmond H. Wu, Michael K. Ng, Tony F. Chan

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

4 Scopus citations

Abstract

We introduce the notion of dense region as distinct and meaningful patterns from given data. Efficient and effective algorithms for identifying such regions are presented. Next, we discuss extensions of the algorithms for handling data streams. Finally, experiments on largescale data streams such as clickstreams are given which confirm that the usefulness of our algorithms.

Original language	English (US)
Title of host publication	Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
Editors	Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang
Publisher	Springer Verlag
Pages	116-120
Number of pages	5
ISBN (Print)	354022064X, 9783540220640
DOIs	https://doi.org/10.1007/978-3-540-24775-3_14
State	Published - 2004
Externally published	Yes
Event	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia Duration: May 26 2004 → May 28 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3056
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
Country/Territory	Australia
City	Sydney
Period	05/26/04 → 05/28/04

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-24775-3_14

Cite this

Yip, A. M., Wu, E. H., Ng, M. K., & Chan, T. F. (2004). An efficient algorithm for dense regions discovery from large-scale data streams. In H. Dai, R. Srikant, & C. Zhang (Eds.), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings (pp. 116-120). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056). Springer Verlag. https://doi.org/10.1007/978-3-540-24775-3_14

Yip, Andy M. ; Wu, Edmond H. ; Ng, Michael K. et al. / An efficient algorithm for dense regions discovery from large-scale data streams. Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. editor / Honghua Dai ; Ramakrishnan Srikant ; Chengqi Zhang. Springer Verlag, 2004. pp. 116-120 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{09245b5a2bad4990aac3c76fde5c869c,

title = "An efficient algorithm for dense regions discovery from large-scale data streams",

abstract = "We introduce the notion of dense region as distinct and meaningful patterns from given data. Efficient and effective algorithms for identifying such regions are presented. Next, we discuss extensions of the algorithms for handling data streams. Finally, experiments on largescale data streams such as clickstreams are given which confirm that the usefulness of our algorithms.",

author = "Yip, {Andy M.} and Wu, {Edmond H.} and Ng, {Michael K.} and Chan, {Tony F.}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.; 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 ; Conference date: 26-05-2004 Through 28-05-2004",

year = "2004",

doi = "10.1007/978-3-540-24775-3_14",

language = "English (US)",

isbn = "354022064X",

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

publisher = "Springer Verlag",

pages = "116--120",

editor = "Honghua Dai and Ramakrishnan Srikant and Chengqi Zhang",

booktitle = "Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings",

address = "Germany",

}

Yip, AM, Wu, EH, Ng, MK & Chan, TF 2004, An efficient algorithm for dense regions discovery from large-scale data streams. in H Dai, R Srikant & C Zhang (eds), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3056, Springer Verlag, pp. 116-120, 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004, Sydney, Australia, 05/26/04. https://doi.org/10.1007/978-3-540-24775-3_14

An efficient algorithm for dense regions discovery from large-scale data streams. / Yip, Andy M.; Wu, Edmond H.; Ng, Michael K. et al.
Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. ed. / Honghua Dai; Ramakrishnan Srikant; Chengqi Zhang. Springer Verlag, 2004. p. 116-120 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056).

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

TY - GEN

T1 - An efficient algorithm for dense regions discovery from large-scale data streams

AU - Yip, Andy M.

AU - Wu, Edmond H.

AU - Ng, Michael K.

AU - Chan, Tony F.

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2004.

PY - 2004

Y1 - 2004

N2 - We introduce the notion of dense region as distinct and meaningful patterns from given data. Efficient and effective algorithms for identifying such regions are presented. Next, we discuss extensions of the algorithms for handling data streams. Finally, experiments on largescale data streams such as clickstreams are given which confirm that the usefulness of our algorithms.

AB - We introduce the notion of dense region as distinct and meaningful patterns from given data. Efficient and effective algorithms for identifying such regions are presented. Next, we discuss extensions of the algorithms for handling data streams. Finally, experiments on largescale data streams such as clickstreams are given which confirm that the usefulness of our algorithms.

UR - http://www.scopus.com/inward/record.url?scp=7444246945&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-24775-3_14

DO - 10.1007/978-3-540-24775-3_14

M3 - Conference contribution

AN - SCOPUS:7444246945

SN - 354022064X

SN - 9783540220640

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

SP - 116

EP - 120

BT - Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings

A2 - Dai, Honghua

A2 - Srikant, Ramakrishnan

A2 - Zhang, Chengqi

PB - Springer Verlag

T2 - 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004

Y2 - 26 May 2004 through 28 May 2004

ER -

Yip AM, Wu EH, Ng MK, Chan TF. An efficient algorithm for dense regions discovery from large-scale data streams. In Dai H, Srikant R, Zhang C, editors, Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Springer Verlag. 2004. p. 116-120. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24775-3_14

An efficient algorithm for dense regions discovery from large-scale data streams

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this