Enhancing SWF for incremental association mining by itemset maintenance

Chia Hui Chang, Shi Hsan Yang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

15 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
主出版物標題Advances in Knowledge Discovery and Data Mining
編輯Kyu-Young Wang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava
發行者Springer Verlag
頁面301-312
頁數12
ISBN(電子)3540047603, 9783540047605
DOIs
出版狀態已出版 - 2003
事件7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of
持續時間: 30 4月 20032 5月 2003

出版系列

名字Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
2637
ISSN(列印)0302-9743

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???event.eventtypes.event.conference???7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
國家/地區Korea, Republic of
城市Seoul
期間30/04/032/05/03

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