Quantitative Evaluation of Web user Session Dissimilarity measures using Medoids based Relational Fuzzy clustering

Abstract

Background/Objectives: Proficient relational clustering of web users’ sessions not only depends on clustering algorithm’s character but also profoundly influenced by the used dissimilarity measures. Therefore, determining the right dissimilarity measure to capture the actual access behaviour of the web user is imperative for the significant clustering.Methods: In this paper, the concept of an augmented session is used to derive different augmented session dissimilarity measures. The quantitative performance evaluation of different session dissimilarity measures are performed using a relational fuzzy c-medoid clustering approach. The intra-cluster and inter-cluster distance based cluster quality ratio is used for performance evaluation. Findings: The experimental results demonstrated that augmented web user session dissimilarity in general, and intuitive augmented session dissimilarity, in particular, performed better than the other dissimilarity measures. Improvements: It is argued that augmented session similarity measures are more realistic and represent session similarities based on the web user’s habits, interest, and expectations as compared to simple binary session similarity measures.