Static load balancing of parallel mining of frequent itemsets using reservoir sampling
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
An effective parallel approach for genetic-fuzzy data mining
Expert Systems with Applications: An International Journal
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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 verylarge 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.