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SBACPAD
2003
IEEE

New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases

9 years 3 months ago
New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases
Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of data in order to produce compact summaries or models of the database. These models are typically used to generate association rules, but recently they have also been used in far reaching domains like e-commerce and bio-informatics. Because databases are increasing in terms of both dimension (number of attributes) and size (number of records), one of the main issues in a frequent itemset mining algorithm is the ability to analyze very large databases. Sequential algorithms do not have this ability, especially in terms of run-time performance, for such very large databases. Therefore, we must rely on high performance parallel and distributed computing. We present new parallel algorithms for frequent itemset mining. Their efficiency is proven through a series of experiments on different parallel environments, th...
Adriano Veloso, Wagner Meira Jr., Srinivasan Parth
Added 05 Jul 2010
Updated 05 Jul 2010
Type Conference
Year 2003
Where sbacpad
Authors Adriano Veloso, Wagner Meira Jr., Srinivasan Parthasarathy
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