Title: Discovery of web usage patterns using fuzzy mountain clustering

Authors: Zahid Ahmed Ansari; Abdul Sattar Syed

Addresses: Department of Computer Science Engineering, P.A. College of Engineering, Mangalore, India ' Department of Computer Science Engineering, Royal Institute of Technology and Science, Hyderabad, India

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.

Keywords: fuzzy clustering; mountain density function; MDF; fuzzy cluster validation; user session clustering; web usage patterns; fuzzy mountain clustering; web usage behaviour; website design; web server logs; fuzzy c-means; fuzzy c-medoids.

DOI: 10.1504/IJBIDM.2016.076413

International Journal of Business Intelligence and Data Mining, 2016 Vol.11 No.1, pp.1 - 18

Received: 20 May 2015
Accepted: 06 Jun 2015

Published online: 06 May 2016 *

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