Grouping Interesting Patterns for Understanding Customer Behaviors

This article presents a highly efficient technique for pattern mining in the realm of customer behavior analysis, termed hybrid clustering patterns for customer behavior analysis (HCP-CBA). It leverages decomposition techniques to uncover relevant patterns by examining correlations among customer transactions within the dataset. Initially, the transaction dataset undergoes decomposition, grouping together transactions exhibiting high correlations. Subsequently, relevant patterns are extracted by applying a pattern mining algorithm represented by Apriori to each group. It incorporates both groups of transactions and shared items between groups. To assess the effectiveness of the HCP-CBA framework, extensive experiments are conducted across customer behavior dataset. The experimental results demonstrate notable reductions in both runtime and scalability. The full code of this research work is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YousIA/ConsumerAnalytics</uri>.