Modeling the Computational Solution of Market Basket Associative Rule Mining Approaches Using Deep Neural Network

A.A. Ojugo^1, and A.O. Eboka²

¹Department of Mathematics/Computer Science, Federal University of Petroleum Resources Effurun, Delta State, Nigeria

²Department of Computer Science Education, Federal College of Education Technical, Asaba, Delta State, Nigeria

Cite this paper:
A.A. Ojugo and A.O. Eboka. Modeling the Computational Solution of Market Basket Associative Rule Mining Approaches Using Deep Neural Network. Digital Technologies. 2018; 3(1):1-8. doi: 10.12691/dt-3-1-1

Abstract

Data is an important property to everyone and lots of it is generated daily. The large amount of data available in the world today, is stored in repositories, databanks, data warehouses etc. Generated data is further on the rise with the Internet, resulting in the consequent explosion of data and its usage. Data convergence over the Internet, has made it more imperative to analyze data relations due to the tremendous sizes that scales up to petabytes of data. But, there exists inherent challenges of extracting useful data from these large repositories. Thus, focal point of this study is to model a rule-based computational solution to the inherent challenge. We thus propose the use of a market basket dataset mining using a hybrid deep learning associative rule mining heuristic.

Keywords:
market basket associative rule mining ARM data mining predictive descriptive deep learning evolutionary

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