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Web users' clustering is an important mining task since it contributes in identifying usage patterns, a beneficial task for a wide range of applications that rely on the web. The purpose of this paper is to examine the usage of Kullback‐Leibler (KL) divergence, an information theoretic distance, as an alternative option for measuring distances in web users clustering.

KL‐divergence is compared with other well‐known distance measures and clustering results are evaluated using a criterion function, validity indices, and graphical representations. Furthermore, the impact of noise (i.e. occasional or mistaken page visits) is evaluated, since it is imperative to assess whether a clustering process exhibits tolerance in noisy environments such as the web.

The proposed KL clustering approach is of similar performance when compared with other distance measures under both synthetic and real data workloads. Moreover, imposing extra noise on real data, the approach shows minimum deterioration among most of the other conventional distance measures.

The experimental results show that a probabilistic measure such as KL‐divergence has proven to be quite efficient in noisy environments and thus constitute a good alternative, the web users clustering problem.

This work is inspired by the usage of divergence in clustering of biological data and it is introduced by the authors in the area of web clustering. According to the experimental results presented in this paper, KL‐divergence can be considered as a good alternative for measuring distances in noisy environments such as the web.