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Frequent pattern mining from high-dimensional data using record space search. (English) Zbl 1157.68358
Gammerman, A. (ed.), Artificial intelligence and applications. Machine learning. As part of the 26th IASTED international multi-conference on applied informatics. Calgary: International Association of Science and Technology for Development (IASTED); Anaheim, CA: Acta Press (ISBN 978-0-88986-710-9/CD-ROM). 327-333 (2008).
Summary: Traditional frequent pattern mining methods have a problem in that the order of calculation exponentially increases with high-dimensional data because of a search using combinations of attributes. The purpose of our work is to develop methods that efficiently extract frequent patterns from very high-dimensional data. We propose HD FPM that can solve the problem using a record space search and a minimum pattern length pruning. The record space search means the search using combinations of records. We can extract frequent patterns from attributes common to the combination of records. We can also reduce a search space using a minimum pattern length pruning.
Several experiments on real microarray datasets show that HD FPM has better performance than pevious closed frequent pattern mining algorithms such as FPclose and CHARM in these in the case that minimum support is low. We also propose parallel HD FPM that can solve the problem using vertical partitioning of a database and parallel processing. Our evaluation of parallel HD FPM performed with a real microarray daraset on 16 PCs has revealed that it is 13 times faster than a sequential one. In conclusion, HD FPM and parallel HD FPM are effective algorithms for frequent pattern mining from high-dimensional data.
For the entire collection see [Zbl 1154.68012].
MSC:
68P05 Data structures
68P15 Database theory
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