Abstract
This work based on the research of Chen et al. who compiled sales data for a UK based online retailer for the years 2009 to 2011. While the work presented by Chen et al. used k means clustering algorithm to generate meaningful customer segments for the year 2011, this research utilised 2010 retail data to generate meaningful business intelligence based on the computed RFM values for the retail data set. We benchmarked the performance of k means and self organizing maps (SOM) clustering algorithms for the filtered target data set. Self organizing maps are utilized to provide a framework for a neural networks computation, which can be benchmarked to the simple k means algorithm used by Chen et al.
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Vohra, R., Pahareeya, J., Hussain, A., Ghali, F., Lui, A. (2020). Using Self Organizing Maps and K Means Clustering Based on RFM Model for Customer Segmentation in the Online Retail Business. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_42
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