Discovery of web usage patterns using fuzzy mountain clustering Online publication date: Fri, 06-May-2016
by Zahid Ahmed Ansari; Abdul Sattar Syed
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 11, No. 1, 2016
Abstract: 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.
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