Using retrieval measures to assess similarity in mining dynamic web clickstreams

While scalable data mining methods are expected to cope with massive Web data, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary stoppages and reconfigurations is still an open challenge. This dynamic and single pass setting can be cast within the framework of mining evolving data streams. In this paper, we explore the task of mining mass user profiles by discovering evolving Web session clusters in a single pass with a recently proposed scalable immune based clustering approach (TECNO-STREAMS), and study the effect of the choice of different similarity measures on the mining process and on the interpretation of the mined patterns. We propose a simple similarity measure that has the advantage of explicitly coupling the precision and coverage criteria to the early learning stages, and furthermore requiring that the affinity of the data to the learned profiles or summaries be defined by the minimum of their coverage or precision, hence requir...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Olfa Nasraoui, Cesar Cardona, Carlos Rojas
• Keywords: Data Mining, KDD 2005, Mining Evolving Data, Mining Evolving User, Scalable Data Mining