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

Mining combined association rules with correlation and market basket analysis can discover customer's buying purchase rules along with frequently correlated, associated-correlated, and independent patterns synchronously which are extraordinarily useful for making everyday's business decisi...

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Bibliographic Details
Published in:Technical review - IETE Vol. 30; no. 1; pp. 53 - 63
Main Authors: Karim, Md. Rezaul, Ahmed, Chowdhury Farhan, Jeong, Byeong-Soo, Choi, Ho-Jin
Format: Journal Article
Language:English
Published: New Delhi Taylor & Francis 01.01.2013
Taylor & Francis Ltd
Subjects:
Algorithms
Associated pattern
Baskets
Business-oriented data
ComMapReduce
Correlated pattern
Data mining
Experiments
Independent pattern
Mines
Null transactions
Studies
Transactional database
ISSN:0256-4602, 0974-5971
Online Access:Get full text
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Summary:Mining combined association rules with correlation and market basket analysis can discover customer's buying purchase rules along with frequently correlated, associated-correlated, and independent patterns synchronously which are extraordinarily useful for making everyday's 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.
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ISSN:0256-4602
0974-5971
DOI:10.4103/0256-4602.107340