CanTree: a tree structure for efficient incremental mining of frequent patterns

Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree based frequent-pattern mining. In this paper, we propose a novel tree str...

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Bibliographic Details
Published inFifth IEEE International Conference on Data Mining (ICDM'05) p. 8 pp.
Main Authors Leung, C.K.-S., Khan, Q.I., Hoque, T.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Cats
Data mining
Database systems
Frequency
Humans
Merging
Test pattern generators
Testing
Transaction databases
Tree data structures
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Summary:Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (canonical-order tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree.
ISBN:9780769522784
0769522785
ISSN:1550-4786
DOI:10.1109/ICDM.2005.38