NDLI: Parallel Mining of Association Rules from Gene Expression Databases

Content Provider	IEEE Xplore Digital Library
Author	Asadpour, M. Sadreddini, M.H. Dastghaibyfard, G.
Copyright Year	2007
Description	Author affiliation: Shiraz Univ., Shiraz (Asadpour, M.; Sadreddini, M.H.; Dastghaibyfard, G.)
Abstract	Gene expression (GE) databases are high-dimensional and have a large number of genes/columns in each profile experiment. Recently, mining association rules (ARM) from these databases has attracted much interest. Several sequential ARM methods have been devised for this purpose, but, to the best of our knowledge, there is no parallel ARM specially designed for GE databases. On the other hand, the existing parallel ARMs are not suitable for such databases because they usually do not take into account the high-dimensionality of the data. In this paper, we propose a parallel ARM specially designed for GE databases. We show that our method parallelizes not only the tasks of reading from databases and computing the supports, but also the tasks of computing the combination between items and generating association rules, which here are very time-consuming. We analyze computation and communication costs, speed-up, and present some experimental results on real databases, as well.
Starting Page	68
Ending Page	73
File Size	302144
Page Count	6
File Format	PDF
ISBN	9780769528748
DOI	10.1109/FSKD.2007.444
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Concurrent computing Computer science Itemsets Data engineering Transaction databases Computational efficiency Data mining Association rules Gene expression Arm
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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