Research on Dynamic Generating Algorithms of Large Itemsets of Distributive Data Mining Architecture

Dynamic generating algorithms of association rules mined large itemsets are presented in this paper. According to the distributive data mining calculation architecture, database is replaced by an order set enumerate tree, and the information of all transactions are kept in the dynamic generating tre...

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Bibliographic Details
Main Authors: Ying-Wu Fang, Yi Wang, Peng-Yang Li, Yan-Jun Lu, Xiu-Bin Zhao, Hui Xu
Format: Conference Proceeding
Language:English
Subjects:
Association rules
Data engineering
Data mining
generating algorithms
Heuristic algorithms
Instruments
Itemsets
Machine learning
Machinery
Relational databases
set enumerate trees
Transaction databases
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Summary:Dynamic generating algorithms of association rules mined large itemsets are presented in this paper. According to the distributive data mining calculation architecture, database is replaced by an order set enumerate tree, and the information of all transactions are kept in the dynamic generating trees. Meantime, the generating enumerate trees flowing a local node of transaction orderly is ensured. The storage and communication traffic are reduced greatly. Therefore, the local space is saved, and the disk operation is reduced through the generating algorithms of large itemsets. By examples and performance analysis of the dynamic generating algorithms introduced, the store space of processed nodes is cut down, and the calculation time of support is also reduced through the traversal process of tree. Thereby, the calculation efficiency of search is improved greatly
ISSN:2160-133X
DOI:10.1109/ICMLC.2006.258659