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Mining Frequent Generalized Patterns for Web Personalization
Proc. of the ECAI 2008 Workshop on Mining Social Data (2008)
  • P. Giannikopoulos, University of Peloponnese
  • I. Varlamis
  • Magdalini Eirinaki, San Jose State University
In this paper we present FGP, an algorithm that combines the powers of an association rule mining algorithm (FP-Growth) and a generalized pattern mining algorithm (GPClose) in order to efficiently generate rules from transaction data. Our Frequent Generalized Pattern (FGP) algorithm considers that all items that appear in a set of transactions, belong to categories organized in a taxonomy. It takes as input the transaction database and the taxonomy of categories and produces generalized association rules that contain transaction items and/or item categories. We demonstrate the operation of the proposed algorithm in the context of personalizing a web site. In this context, the transaction database contains user clickstream information and the hierarchy of item types is a thematic taxonomy of web pages. The algorithm generates frequent item sets comprising of both web pages and categories. The results can be used to generate association rules and consequently recommendations for the users. We experimentally evaluate the proposed algorithm using web log data collected from a newspaper web site.
Publication Date
July, 2008
Citation Information
P. Giannikopoulos, I. Varlamis and Magdalini Eirinaki. "Mining Frequent Generalized Patterns for Web Personalization" Proc. of the ECAI 2008 Workshop on Mining Social Data (2008)
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