Abstract
Due to the continuous proliferation of e-businesses, there is intense competition among organizations to attract and retain customers. Analyses of the web server logs of these organizations are critical for obtaining insights into web usage behavior, which can support the design of more attractive web structures. In this study, we propose a mountain density function (MDF)-based fuzzy clustering framework for discovering user session clusters in web log data. The major steps in this framework include web log preprocessing, MDF-based discovery of fuzzy user session clusters, and validation of these clusters. To consider the high dimensionality of user session data, we propose a fuzzy approach for assigning weights to user sessions. Fuzzy c-means (FCM) and fuzzy c-medoids (FCMed) algorithms are used to cluster the user sessions. The selection of suitable initial cluster centers is a major challenge for these methods, so we propose MDF-based FCM (MDFCM) and FCMed (MDFCMed) algorithms to overcome this problem. MDF-based clustering is also used to estimate the number of clusters. Our results clearly indicate that the quality of the clusters formed using the proposed algorithms is much better in terms of various validity measures compared with the FCM and FCMed algorithms.