Enhancing SWF for incremental association mining by itemset maintenance

Chia Hui Chang, Shi Hsan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations


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 languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
EditorsKyu-Young Wang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava
PublisherSpringer Verlag
Number of pages12
ISBN (Electronic)3540047603, 9783540047605
StatePublished - 2003
Event7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of
Duration: 30 Apr 20032 May 2003

Publication series

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


Conference7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
Country/TerritoryKorea, Republic of


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