Customer Transaction Pattern Analysis Using Data Mining: An Integrated K-Means and FP-Growth Approach

This study addresses the fundamental challenge of leveraging high-volume transaction records to achieve simultaneous business goals: enhancing personalisation and driving strategic financial valuation. By applying a structured data mining methodology to the Online Retail II Dataset, this research successfully transformed complex transaction records into actionable strategic intelligence. The segmentation process involved IQR-based outlier exclusion and Standard Scaling of RFM features to mitigate data skew. This prepared data was then analysed using K-Means clustering for market segmentation and the FP-Growth algorithm for generating association rules. Key findings confirmed the Pareto Principle, showing that the top 27.6% of customers generate most of the revenue, justifying resource allocation toward high-value segments (VIP/Loyal Champions). Furthermore, the FP-Growth model generated exceptionally strong association rules (Lift up to 52.79), which were estimated to drive over $124,000 in incremental annual revenue through optimised cross-selling. The results validate the quantitative efficacy of integrating these data mining techniques to provide a financial quantifiable roadmap for optimising marketing and operational strategies in the modern digital economy.

Data Mining, Customer Transaction Analysis, K-Means Clustering, FP-Growth Algorithm

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