Clustering and Semi-Supervised Classification for Clickstream Data via Mixture Models

DOI10.48550/ARXIV.1802.04849arXiv1802.04849MaRDI QIDQ143898

Michael P. B. Gallaugher, Paul D. McNicholas

Publication date: 13 February 2018

Abstract: Finite mixture models have been used for unsupervised learning for some time, and their use within the semi-supervised paradigm is becoming more commonplace. Clickstream data is one of the various emerging data types that demands 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 semi-supervised 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 to the discrete time approach, using simulated and real data.