Online shopping customer data analysis by using association rules and cluster analysis

Authors:
Serhat Güden;Umman Tugba Gursoy
Affiliations:
Institute of Business Administration, Istanbul University, Istanbul, Turkey;Institute of Business Administration, Istanbul University, Istanbul, Turkey
Venue:
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Year:
2013

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Abstract

Data Mining is the process of exploration and analysis of large quantities of data in order to discover meaningful patterns and rules. Data mining is considered as the only solution towards efficient use of increasing amounts of data worldwide. The process of converting data into information is achieved by means of data mining. In this study, first the concept of data mining is presented, then CRISP-DM process are described. In this paper Cluster Analysis and Association Rules are used to analyze the data. k-means Algorithm, Confidence and Support Ratios are theoretically explained and these techniques applied to a data set obtained from 314 customers from 7 regions of Turkey to identify their profile.