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Blending Association Rules for Knowledge Discovery in Big Data

Blending Association Rules for Knowledge Discovery in Big Data
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Author(s): Ali Anaissi (The University of Sydney, Australia) and Madhu Goyal (The University of Technology Sydney, Australia)
Copyright: 2019
Pages: 18
Source title: Enabling Technologies and Architectures for Next-Generation Networking Capabilities
Source Author(s)/Editor(s): Mahmoud Elkhodr (Central Queensland University, Australia)
DOI: 10.4018/978-1-5225-6023-4.ch012

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Abstract

Data mining techniques have been widely applied in several domains to support a variety of business-related applications such as market basket analysis. For instance, basket market transaction accumulate large amounts of customer purchase data from their day-to-day operations. This paper delivers a strategy for the implementation of a systematic analysis framework built on the established principles used in data mining and machine learning areas.We employ Apriori and FP-growth algorithms coupled with support vector machine to implement our recommendation systems. Experiments are done using a real market dataset and the 0.632+ bootstrap method is used here in order to evaluate our framework. The obtained results suggest that the proposed framework will be able to generate benefits for grocery chain using a real-world grocery store data. FP-growth algorithm shows better performance over Apriori in terms of time complexity.

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