Smart Miner: a new framework for mining large scale web usage data
Proceedings of the 18th international conference on World wide web
Discovering better navigation sequences for the session construction problem
Data & Knowledge Engineering
Using weighted clustering and symbolic data to evaluate institutes’s scientific production
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data
Expert Systems with Applications: An International Journal
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The analysis of a web site based on usage data is an important task as it provides insight into the organization of the site and its adequacy regarding user needs. This allows the relationship between prior categories and user browsing patterns to be explored. In this paper we propose an approach for discovering the profiles of visitor groups. To this end, we begin by mapping user interests into symbolic objects, which is the basis of the Symbolic Data Analysis and represents here a successful interaction of the user with the web site. We then identify groups of users with similar behavior by means of a dynamic clustering approach applying a context dependent dissimilarity measure. The method was applied to identify visitor groups of a web site in the educational domain and also to analyze the traces of different user behavior.