document: 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 requiring that the learned profiles are simultaneously precise and complete , with no compromises . In our experiments , we study the task of mining evolving user profiles from Web clickstream data ( web usage mining ) in a single pass , and under different trend sequencing scenarios , showing that compared oto the cosine similarity measure , the proposed similarity measure explicitly based on precision and coverage allows the discovery of more correct profiles at the same precision or recall quality levels .
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