Abstract

Clustering means grouping similar objects into groups such that objects within a same group bear similarity to each other while objects in di#erent groups are dissimilar to each other. As an important component of data mining, much research on clustering has been conducted in di#erent disciplines. In the context of web mining, clustering could be used to cluster similar clickstreams to determine learning behaviours in the case of e-learning, or general site access behaviours in e-commerce or other on-line applications. Most of the algorithms presented in the literature to deal with clustering web sessions treat sessions as sets of visited pages within a time period and don't consider the sequence of the click-strem visitation. This has a significant consequence when comparing similarities between web sessions. We propose in this paper a new algorithm based on sequence alignment to measure similarities between web sessions where sessions are chronologically ordered sequences of page accesses.

Citations