Blending Association Rules for Knowledge Discovery in Big Data
- IGI Global
- Publication Type:
- Enabling Technologies and Architectures for Next-Generation Networking Capabilities, 2019, pp. 254 - 271 (18)
- Issue Date:
|Blending Association Rules for Knowledge Discovery in Big Data.pdf||Published Version||809.74 kB|
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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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