Fuzzy c-Least Medians clustering for discovery of web access patterns from web user sessions data

Abstract

Mining web usage data of e-business organizations is essential to provide knowledge about clients’ web utilization patterns, which can help these businesses in landing at vital business choices. Because of non-deterministic web access behavior of web clients, web user session data is usually noisy and imperfect. Such imperfection has a negative impact on pattern discovery process. One of the real issues associated with the prevalently used Fuzzy c-Means (FCM) and Fuzzy c-Medoids (FCMdd) methods is that they are not robust against the noise, because a single outlier object could lead to a very different clustering result. In this research we propose a robust Fuzzy c-Least Medians (FCLMdn) clustering framework to deal with the user session data contaminated with noise and outlier user session objects, with the objective of improving the quality of the extracted patterns. To deal with the high dimensionality of user session data which may contain noise and outliers, a fuzzy set theoretic approach for assigning fuzzy weights to user sessions and associated URLs has been proposed. Our results clearly indicate that quality of user session clusters formed using FCLMdn algorithm is much better than those using FCM and FCMdd algorithms in terms of various cluster validity indices.