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Session Clustering Using Mixtures of Proportional Hazards Models

By Patrick Mair and Marcus Hudec


Emanating from classical Weibull mixture models we propose a framework for clustering survival data with various proportionality restrictions imposed. By introducing mixtures of Weibull proportional hazards models on a multivariate data set a parametric cluster approach based on the EM-algorithm is carried out. The problem of non-response in the data is considered. The application example is a real life data set stemming from the analysis of a world-wide operating eCommerce application. Sessions are clustered due to the dwell times a user spends on certain page-areas. The solution allows for the interpretation of the navigation behavior in terms of survival and hazard functions. A software implementation by means of an R package is provided. (author´s abstract)Series: Research Report Series / Department of Statistics and Mathematic

Topics: proportional hazards models / Weibull mixture models / EM-algorithm / incomplete data / Web usage mining
Publisher: Department of Statistics and Mathematics, WU Vienna University of Economics and Business
Year: 2008
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