Customer Visit Segmentation Based on Clustering and Association Rules

Kale, Vishakha Balkrishna (2020) Customer Visit Segmentation Based on Clustering and Association Rules. Masters thesis, Dublin, National College of Ireland.

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Abstract

Retail businesses are highly involved with customers, where customers can contribute in the profit and loss of the business thus makes them an important factor to be studied and analysed. Among the various factors studied for customer analysis such as market basket analysis and Customer Segmentation, Customer visit segmentation can also be considered as a meaningful analysis of customers and their shopping visits. Where Customer segmentation explains the motive behind each visit of customer to a retail shop, this research aims at adding a useful analysis to these visits by studying them further with Association rules. The aim of this research is to analyse the customer visits formed with k-means clustering by further analysis using Apriori and Eclat algorithm. This study gives a contribution towards deep analysis of customer visit segmentation using association rules and attempts to improve the performance using Eclat algorithm.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Dan English
Date Deposited:	20 Jan 2021 16:07
Last Modified:	20 Jan 2021 16:07
URI:	https://norma.ncirl.ie/id/eprint/4402

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