Handling Transactional Data Features via Associative Rule Mining for Mobile Online Shopping Platforms

Transactional data processing is often a reflection of a consumer's buying behavior. The relational records if properly mined, helps business managers and owners to improve their sales volume. Transaction datasets are often rippled with the inherent challenges in their manipulation, storage and...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2024, Vol.15 (3)
Main Authors: Akazue, Maureen Ifeanyi, Okofu, Sebastina Nkechi, Ojugo, Arnold Adimabua, Ejeh, Patrick Ogholuwarami, Odiakaose, Christopher Chukwufunaya, Emordi, Frances Uche, Ako, Rita Erhovwo, Geteloma, Victor Ochuko
Format: Article
Language:English
Subjects:
Batch processing
Big Data
Computer science
Consumers
Data mining
Data processing
Datasets
Decision making
Decision support systems
Drift
Electronic commerce
Evolution
Product specifications
Purchasing
Shopping
Variables
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Summary:Transactional data processing is often a reflection of a consumer's buying behavior. The relational records if properly mined, helps business managers and owners to improve their sales volume. Transaction datasets are often rippled with the inherent challenges in their manipulation, storage and handling due to their infinite length, evolution of product features, evolution in product concept, and oftentimes, a complete drift away from product feat. The previous studies' inability to resolve many of these challenges as abovementioned, alongside the assumptions that transactional datasets are presumed to be stationary when using the association rules – have been found to also often hinder their performance. As it deprives the decision support system of the needed flexibility and robust adaptiveness to manage the dynamics of concept drift that characterizes transaction data. Our study proposes an associative rule mining model using four consumer theories with RapidMiner and Hadoop Tableau analytic tools to handle and manage such large data. The dataset was retrieved from Roban Store Asaba and consists of 556,000 transactional records. The model is a 6-layered framework and yields its best result with a 0.1 value for both the confidence and support level(s) at 94% accuracy, 87% sensitivity, 32% specificity, and a 20-second convergence and processing time.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0150354