An Efficient Distributed Programming Model for Mining Useful Patterns in Big Datasets

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Mining combined association rules with correlation and market basket analysis can discover customers buying purchase rules along with frequently correlated, associated-correlated, and independent patterns synchronously which are extraordinarily useful for making everydays business decisions. However, due to the main memory bottleneck in single computing system, existing approaches fail to handle big datasets. Moreover, most of them cannot overcome the screenings and overhead of null transactions; hence, performance degrades drastically. In this paper, considering these limitations, we propose a distributed programming model for mining business-oriented transactional datasets by using an improved MapReduce framework on Hadoop, which overcomes not only the single processor and main memory-based computing, but also highly scalable in terms of increasing database size. Experimental results show that the technique proposed and developed in this paper are feasible for mining big transactional datasets in terms of time and scalability.
Publisher
MEDKNOW PUBLICATIONS & MEDIA PVT LTD
Issue Date
2013-01
Language
English
Article Type
Article
Keywords

DATABASES

Citation

IETE TECHNICAL REVIEW, v.30, no.1, pp.53 - 63

ISSN
0256-4602
DOI
10.4103/0256-4602.107340
URI
http://hdl.handle.net/10203/174906
Appears in Collection
CS-Journal Papers(저널논문)
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