Tham khảo tài liệu 'data analysis machine learning and applications episode 3 part 3', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Analysis of Dwell Times in Web Usage Mining Patrick Mair1 and Marcus Hudec2 1 Department of Statistics and Mathematics and ec3 Wirtschaftuniversitat Wien Augasse 2-6 1090 Vienna Austria 2 Department of Scientific Computing and ec3 University of Vienna Universitatsstr. 5 1010 Vienna Austria Abstract. In this contribution we focus on dwell times a user spends on various areas of a web site within a session. We assume that dwell times may be adequately modeled by a Weibull distribution which is a flexible and common approach in survival analysis. Furthermore we introduce heterogeneity by various parameterizations of dwell time densities by means of proportional hazards models. According to these assumptions the observed data stem from a mixture of Weibull densities. Estimation is based on EM-algorithm and model selection may be guided by BIC. Identification of mixture components corresponds to a segmentation of users sessions. A real life data set stemming from the analysis of a world wide operating eCommerce application is provided. The corresponding computations are performed with the mixPHM package in R. 1 Introduction Web Usage Mining focuses on the analysis of visiting behavior of users on a web site. Common starting point are the so called click-stream data which are derived from web-server logs and may be viewed as the electronic trace a user leaves on a web site. Adequate modeling of the dynamics of browsing behavior is of particular relevance for the optimization of eCommerce applications. Recently Montgomery et al. 2004 proposed a dynamic multinomial probit model of navigation patterns which lead to an remarkable increase of conversion rates. Park and Fader 2004 developed multivariate exponential-gamma models which enhance cross-site customer acquisition. These papers indicate the potential that such approaches offer for webshop providers. In this paper we will focus on modeling dwell times . the time a
