Clustering Web Sessions by Sequence Alignment

Clustering means grouping similar objects into groups such that objects within a same group bear similarity to each other while objects in different groups are dissimilar to each other. As an important component of data mining, much research on clustering has been conducted in different 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.