Discovery of web usage patterns using fuzzy mountain clustering
Zahid Ansari, Abdul Sattar Syed · International Journal of Business Intelligence and Data Mining · 2016
Analysis of web server logs of e-business organisations is critical to provide insight into users' web usage behaviour which can assist in designing most attractive websites. In this article, a mountain density function (MDF)-based fuzzy clustering framework to discover user session clusters from web logs is proposed. Major steps in this framework include web log preprocessing, MDF-based discovery of user session clusters and their validation. To deal with high dimensionality of user sessions, a fuzzy approach for assigning weights to user sessions has been proposed. For the discovery of user session clusters, fuzzy c-means (FCM) and fuzzy c-medoids (FCMed) algorithms are explored. Since the selection of suitable initial cluster centres is a big challenge, MDF-based fuzzy c-means (MDFCM) and fuzzy c-medoids (MDFCMed) algorithms are proposed to overcome this problem. Our results show that quality of clusters formed using MDFCM/MDFCMed is much better than FCM and FCMed.