Towards efficient data re-mining (DRM)

Jiming Liu; Jian Yin

doi:10.1007/3-540-45357-1_43

Towards efficient data re-mining (DRM)

Jiming Liu, Jian Yin

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

9 Citations (Scopus)

Abstract

The problem that we tackle here is a practical one: When users interactively mine association rules, it is often the case that they have to continuously tune two thresholds: minimum support and minimum confidence, which describe the users' changing requirements. In this paper, we present an efficient data re-mining (DRM) technique for updating previously discovered association rules in light of threshold changes.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings
Editors	David Cheung, Graham J. Williams, Qing Li
Publisher	Springer Verlag
Pages	406-412
Number of pages	7
ISBN (Print)	3540419101, 9783540419105
DOIs	https://doi.org/10.1007/3-540-45357-1_43
Publication status	Published - 2001
Event	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 - Hong Kong, Hong Kong, China Duration: 16 Apr 2001 → 18 Apr 2001 https://link.springer.com/book/10.1007/3-540-45357-1 (Conference Proceedings)

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2035
ISSN (Print)	0302-9743

Conference

Conference	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001
Country/Territory	Hong Kong, China
City	Hong Kong
Period	16/04/01 → 18/04/01
Internet address	https://link.springer.com/book/10.1007/3-540-45357-1 (Conference Proceedings)

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10.1007/3-540-45357-1_43

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Liu, J., & Yin, J. (2001). Towards efficient data re-mining (DRM). In D. Cheung, G. J. Williams, & Q. Li (Eds.), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings (pp. 406-412). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2035). Springer Verlag. https://doi.org/10.1007/3-540-45357-1_43

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title = "Towards efficient data re-mining (DRM)",

abstract = "The problem that we tackle here is a practical one: When users interactively mine association rules, it is often the case that they have to continuously tune two thresholds: minimum support and minimum confidence, which describe the users' changing requirements. In this paper, we present an efficient data re-mining (DRM) technique for updating previously discovered association rules in light of threshold changes.",

author = "Jiming Liu and Jian Yin",

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_43",

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

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Liu, J & Yin, J 2001, Towards efficient data re-mining (DRM). in D Cheung, GJ Williams & Q Li (eds), 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), vol. 2035, Springer Verlag, pp. 406-412, 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, Hong Kong, Hong Kong, China, 16/04/01. https://doi.org/10.1007/3-540-45357-1_43

Towards efficient data re-mining (DRM). / Liu, Jiming; Yin, Jian.
Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. ed. / David Cheung; Graham J. Williams; Qing Li. Springer Verlag, 2001. p. 406-412 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2035).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AB - The problem that we tackle here is a practical one: When users interactively mine association rules, it is often the case that they have to continuously tune two thresholds: minimum support and minimum confidence, which describe the users' changing requirements. In this paper, we present an efficient data re-mining (DRM) technique for updating previously discovered association rules in light of threshold changes.

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Towards efficient data re-mining (DRM)

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