Mining Evolving Web Clickstreams with Explicit Retrieval Similarity Measures
Olfa Nasraoui, Cesar Cardona, Carlos Rojas · 2004
Data on the Web is noisy, huge, and dynamic. This poses enormous challenges to most data mining techniques that try to extract patterns from this data. While scalable data mining methods are expected to cope with the size challenge, 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. The harsh restrictions imposed by the "you only get to see it once" constraint on stream data calls for different computational models that may bring some interesting surprises when it comes to the behavior of some well known similarity measures during clustering. In this paper, we explore the task of mining evolving clusters in a single pass with a new 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 explicitely 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 simulations, 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.