NORMA eResearch @NCI Library

Clustering Based Approach to Enhance Association Rule Mining

Kanhere, Samruddhi Shailesh, Sahni, Anu, Stynes, Paul and Pathak, Pramod (2021) Clustering Based Approach to Enhance Association Rule Mining. In: 28th Conference of Open Innovations Association (FRUCT), 27-29 January 2021, Moscow, Russia.

Full text not available from this repository.
Official URL: https://ieeexplore.ieee.org/document/9347577

Abstract

Association rule mining algorithms such as Apriori and FPGrowth are extensively being used in the retail industry to uncover consumer buying patterns. However, the scalability of these algorithms to deal with the voraciously increasing data is the major challenge. This research presents a novel Clustering based approach by reducing the dataset size as a solution. The products are clustered based on their frequency and price. Another important aspect of this study is to find interesting rules by performing differential market basket analysis to identify association rules which are likely ignored in the trivial approach. When using a cluster-based approach, it is observed that the same set of rules can be generated by using only 7% of the total 16210 items, which in turn directly contributes to reducing the processing overheads and thus reducing the computation time. Furthermore, results obtained from differential market basket analysis have highlighted a few interesting rules which were missing from the original set of rules. A clustering-based approach used in this study not only consists of frequent items but also considers their contribution to the overall revenue generation by considering its price. In addition to this, the least contributing product exclusion rate is also improved from 45% to 93%. These results evidently suggest that the computation cost can be significantly reduced, and more accurate rules can be generated by applying differential market basket analysis.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

H Social Sciences > HF Commerce > Marketing > Consumer Behaviour
Divisions: School of Computing > Staff Research and Publications
Depositing User: Clara Chan
Date Deposited: 27 Jul 2021 15:55
Last Modified: 27 Jul 2021 16:24
URI: http://norma.ncirl.ie/id/eprint/4902

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