Segmentation Of Online Shoppers By Means Of An Integrated Data Mining Approach: A Case Study

Year 2008, Volume: 10 Issue: 3, 74 - 88, 01.09.2008

Çağatan Taşkın Gül Gökay Emel

Abstract

Recent findings indicate that online shopping is increasing significantly all over the world. Segmentation of online shoppers is an important issue of online firms. This paper handles this issue related to internet-related lifestyle descriptors which are effective in determining segments by an integrated data mining approach. The integrated data mining approach which is used in this study consists of self-organizing map (Kohonen) neural network and association rule mining method which are integrated to identify segments of online shoppers. Similar to the international trends, online shopping has become one of the most noticeable yields of internet in Turkey and the research is conducted in a highly industrialized region. For this multi-dimensional analysis, a visual and a robust data mining software Clementine 8.1 is used for the integrated segmentation task in data mining.

Keywords

Segmentation, Online Shopping, Data Mining, Kohonen, Association Rules

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