Parallel and Distributed Algorithms for Frequent Pattern Mining in Large Databases

Mining frequent patterns (FP) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. A significant number of parallel and distributed FP mining algorithms have been proposed, when the database is large and/or distributed. Among t...

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Bibliographic Details
Published in:Technical review - IETE Vol. 26; no. 1; pp. 55 - 66
Main Authors: Tanbeer, Syed Khairuzzaman, Ahmed, Chowdhury Farhan, Jeong, Byeong-Soo
Format: Journal Article
Language:English
Published: New Delhi Taylor & Francis 01.01.2009
Taylor & Francis Ltd
Subjects:
Algorithms
Computer engineering
Data mining
Frequent patterns
Knowledge discovery
Large-scale databases
Parallel and distributed processing
Studies
Tree restructuring
Trees
ISSN:0256-4602, 0974-5971
Online Access:Get full text
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Summary:Mining frequent patterns (FP) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. A significant number of parallel and distributed FP mining algorithms have been proposed, when the database is large and/or distributed. Among them, parallelization of the FP-growth algorithm using the FP-tree has been proved to be more efficient, when compared to the Apriori -based approaches. However, the FP-tree based techniques suffer from two major limitations - multiple database scans requirement (i.e., high I/O cost) and huge communication overhead. Therefore, in this paper, we propose a novel tree structure, called PP-tree (Parallel Pattern tree) that significantly reduces the I/O cost by capturing the database contents with a single scan and facilitates efficient FP-growth mining on it. Our parallel algorithm works independently at each local site and merges the locally generated global frequent patterns at the final stage, thereby reducing inter-processor communication overhead and getting a high degree of parallelism. Extensive experimental study on datasets of different types reflects that parallel and distributed FP mining with our PP-tree is highly efficient on large databases.
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ISSN:0256-4602
0974-5971
DOI:10.4103/0256-4602.48469