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ICDM
2003
IEEE

Mining Frequent Itemsets in Distributed and Dynamic Databases

14 years 4 months ago
Mining Frequent Itemsets in Distributed and Dynamic Databases
Traditional methods for frequent itemset mining typically assume that data is centralized and static. Such methods impose excessive communication overhead when data is distributed, and they waste computational resources when data is dynamic. In this paper we present what we believe to be the first unified approach that overcomes these assumptions. Our approach makes use of parallel and incremental techniques to generate frequent itemsets in the presence of data updates without examining the entire database, and imposes minimal communication overhead when mining distributed databases. Further, our approach is able to generate both local and global frequent itemsets. This ability permits our approach to identify high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.
Matthew Eric Otey, Chao Wang, Srinivasan Parthasar
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where ICDM
Authors Matthew Eric Otey, Chao Wang, Srinivasan Parthasarathy, Adriano Veloso, Wagner Meira Jr.
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