Mining online shopping patterns and communities

Authors:
Keivan Kianmehr;X. Peng;Chris Luce;Justin Chung;Nam Pham;Walter Chung;Reda Alhajj;Jon Rokne;Ken Barker
Affiliations:
University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada and Global University, Beirut, Lebanon;University of Calgary, Calgary, Alberta, Canada;University of Calgary, Calgary, Alberta, Canada
Venue:
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Year:
2009

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Abstract

The great increase in online transactions and the thousands of online retailers has created a great demand for companies to gain competitive advantage. An easy way for a company to gain customer advantage is through the use of data mining. Due to this high demand we have developed a prototypical tool to help with the analysis of these online transactions. From the raw data generated by running these transactions we are able to find consumer trends and shopping patterns by using hierarchical clustering and association rules mining algorithm. The focus of this research is to demonstrate how this development can be useful and effective in a business situation for companies to gain competitive advantage.