Enhancing SWF for incremental association mining by itemset maintenance

Chia Hui Chang; Shi Hsan Yang

doi:10.1007/3-540-36175-8_30

Enhancing SWF for incremental association mining by itemset maintenance

Chia Hui Chang, Shi Hsan Yang

Department of Computer Science and Information Engineering

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

15 Scopus citations

Abstract

Incremental association mining refers to the maintenance and utilization of the knowledge discovered in the previous mining operations for later association mining. Sliding window filtering (SWF) is a technique proposed to filter false candidate 2-itemsets by segmenting a transaction database into partitions. In this paper, we extend SWF by incorporating previously discovered information and propose two algorithms to boost the performance for incremental mining. The first algorithm FI-SWF (SWF with Frequent Itemset) reuses the frequent itemsets of previous mining task to reduce the number of new candidate itemsets that have to be checked. The second algorithm CI SWF (SWF with Candidate Itemset) reuses the candidate itemsets from the previous mining task. Experiments show that the new proposed algorithms are significantly faster than SWF.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Editors	Kyu-Young Wang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava
Publisher	Springer Verlag
Pages	301-312
Number of pages	12
ISBN (Electronic)	3540047603, 9783540047605
DOIs	https://doi.org/10.1007/3-540-36175-8_30
State	Published - 2003
Event	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of Duration: 30 Apr 2003 → 2 May 2003

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2637
ISSN (Print)	0302-9743

Conference

Conference	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
Country/Territory	Korea, Republic of
City	Seoul
Period	30/04/03 → 2/05/03

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10.1007/3-540-36175-8_30

Cite this

Chang, C. H., & Yang, S. H. (2003). Enhancing SWF for incremental association mining by itemset maintenance. In K-Y. Wang, J. Jeon, K. Shim, & J. Srivastava (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 301-312). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2637). Springer Verlag. https://doi.org/10.1007/3-540-36175-8_30

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Chang, CH & Yang, SH 2003, Enhancing SWF for incremental association mining by itemset maintenance. in K-Y Wang, J Jeon, K Shim & J Srivastava (eds), Advances in Knowledge Discovery and Data Mining. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2637, Springer Verlag, pp. 301-312, 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003, Seoul, Korea, Republic of, 30/04/03. https://doi.org/10.1007/3-540-36175-8_30

Enhancing SWF for incremental association mining by itemset maintenance. / Chang, Chia Hui; Yang, Shi Hsan.
Advances in Knowledge Discovery and Data Mining. ed. / Kyu-Young Wang; Jongwoo Jeon; Kyuseok Shim; Jaideep Srivastava. Springer Verlag, 2003. p. 301-312 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2637).

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

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AB - Incremental association mining refers to the maintenance and utilization of the knowledge discovered in the previous mining operations for later association mining. Sliding window filtering (SWF) is a technique proposed to filter false candidate 2-itemsets by segmenting a transaction database into partitions. In this paper, we extend SWF by incorporating previously discovered information and propose two algorithms to boost the performance for incremental mining. The first algorithm FI-SWF (SWF with Frequent Itemset) reuses the frequent itemsets of previous mining task to reduce the number of new candidate itemsets that have to be checked. The second algorithm CI SWF (SWF with Candidate Itemset) reuses the candidate itemsets from the previous mining task. Experiments show that the new proposed algorithms are significantly faster than SWF.

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Enhancing SWF for incremental association mining by itemset maintenance

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