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Published Articles >> Table of Contents >> Abstract
15th Symposium on Computer Architecture and High Performance Computing (SBAC-PAD'03)
p. 158
New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases
Adriano Veloso, Universidade Federal de Minas Gerais
Wagner Meira Jr., Universidade Federal de Minas Gerais
Srinivasan Parthasarathy, Ohio State University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CAHPC.2003.1250334
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| Abstract |
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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, that range from shared-memory multiprocessors machines to a set of SMP clusters connected together through a high speed network.We also briefly discuss an application of our algorithms to the analysis of large databases collected by a Brazilian web portal.
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 Additional Information
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Citation:
Adriano Veloso, Wagner Meira Jr., Srinivasan Parthasarathy,
"New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases,"
sbac-pad,
p. 158,
15th Symposium on Computer Architecture and High Performance Computing (SBAC-PAD'03),
2003
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