Mining online shopping patterns and communities
Authors: 
Keivan Kianmehr, X. Peng, Chris Luce, Justin Chung, Nam Pham, Walter Chung, Reda Alhajj, Jon Rokne, Ken BarkerAuthors Info & Claims
Keivan Kianmehr, X. Peng, Chris Luce, Justin Chung, Nam Pham, Walter Chung, Reda Alhajj, Jon Rokne, Ken BarkerAuthors Info & ClaimsPages 400 - 404
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.
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- Mining online shopping patterns and communities
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Information & Contributors
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Published In
December 2009
763 pages
ISBN:9781605586601
DOI:10.1145/1806338
- General Chair:
- Gabriele Kotsis,
- Program Chairs:
- David Taniar,
- Eric Pardede
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Publication History
Published: 14 December 2009
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Conference
iiWAS '09
Sponsor:
iiWAS '09: 11th International Conference on Information Integration and Web-based Applications & Services
December 14 - 16, 2009
Kuala Lumpur, Malaysia
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