FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops

Abstract

1Malasowe, Bridget Ogheneovo,, 2Okpako, Abugor Ejaita, 3 , 4Ejeh, Patrick Ogholuwarami, 5Ojugo, Arnold Adimabua & 6 1Department of Computer Science, University of Delta, Agbor, Nigeria 2Department of Cyber Security, University of Delta, Agbor, Nigeria 3Department of Cyber Security, Faculty of Computing, Delta State University of Science and Technology, Ozoro. Nigeria. 4Department of Computer Science, Dennis Osadebay University, Anwai-Asaba, Nigeria 5 Department of Computer Science, Federal University of Petroleum Resources Effurun, Nigeria 6 Department of Computer Science, Federal University of Petroleum Resources Effurun, Nigeria E-mails: 1bridget.malasowe@unidel.edu.ng; 2ejaita.okpako@unidel.edu.ng; 3okpormd@dsust.edu.ng, 4 patrick.ejeh@dou.edu.ng; 5ojugo.arnold@fupre.edu.ng, 6ako.rita@fupre.edu.ng ABSTRACT Transaction data often is a true presentation of consumers’ buying behavior, stored as a set of relational records, which properly harnessed via mining – can help businesses improve their sales volume as a decision support system. Managing such a system can pose many issues to biz such as feature evolution, concept evolution, concept drift, and infinite data length – and often makes it impractical to effectively store such big-data. To curb this, previous studies have assumed data to be stationary in using associative rule mining. This has deprived such systems of the flexibility and adaptiveness required to handle the dynamics of concept drift that characterizes transaction datasets. Our study thus proposes a basket frequent pattern growth trained associative rule mining model to handle large data. The dataset was retrieved from the Delta-Mall Asaba and consists of 556,000 transaction consumer records. The model consists of six-layers, and yields the best result with a 0.1 value for both the confidence and support level(s) at a 94% accuracy, sensitivity of 87%, and a specificity of 32% with a 20-second convergence and processing time. Keywords: Big-Data Analysis, Feature Evolution, Inventory Model, Market Basket Analysis, Businesses, Transactional Data Streams, Concept Drift CISDI Journal Reference Format Malasowe, B.O., Okpako, A.E., Okpor, M.D., Ejeh, P.O., Ojugo, A.A. & Ako, R.E. (2024): FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 15-28. dx.doi.org/10.22624/AIMS/CISDI/V15N2P2-1. Available online at www.isteams.net/cisdijournal