Algorithms for clustering clickstream data

Authors:
Panagiotis Antonellis;Christos Makris;Nikos Tsirakis
Affiliations:
Department of Computer Engineering & Informatics, University of Patras, 26500 Rio-Patras, Greece;Department of Computer Engineering & Informatics, University of Patras, 26500 Rio-Patras, Greece;Department of Computer Engineering & Informatics, University of Patras, 26500 Rio-Patras, Greece
Venue:
Information Processing Letters
Year:
2009

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Abstract

Clustering is a classic problem in the machine learning and pattern recognition area, however a few complications arise when we try to transfer proposed solutions in the data stream model. Recently there have been proposed new algorithms for the basic clustering problem for massive data sets that produce an approximate solution using efficiently the memory, which is the most critical resource for streaming computation. In this paper, based on these solutions, we present a new model for clustering clickstream data which applies three different phases in the data processing, and is validated through a set of experiments.