A pattern decomposition algorithm for data mining of frequent patterns
Knowledge and Information Systems
Frequent Pattern Mining on Message Passing Multiprocessor Systems
Distributed and Parallel Databases
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Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-testapproach. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass making it more efficient to mine frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing dataset. Our empirical evaluation shows that the algorithmoutperforms Apriori by one order of magnitude and is faster than FP-tree. Further, PD is more scalable than both Apriori and FP-tree.