Clustering and semi-supervised classification for clickstream data via mixture models

Abstract

Finite mixture models have been used for unsupervised learning for some time, and their use within the semisupervised paradigm is becoming more commonplace. Clickstream data are one of the various emerging data types that demand particular attention because there is a notable paucity of statistical learning approaches currently available. A mixture of first-order continuous-time Markov models is introduced for unsupervised and semisupervised learning of clickstream data. This approach assumes continuous time, which distinguishes it from existing mixture model-based approaches; practically, this allows account to be taken of the amount of time each user spends on each webpage. The approach is evaluated and compared with the discrete-time approach, using simulated and real data.