Performance Evaluation of an Augmented Session Dissimilarity Matrix of Web User Sessions Using Relational Fuzzy C-means clustering
نویسندگان
چکیده
In this paper, the concept of an augmented session is used to derive different augmented session similarity measures. It is believed 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 cosine measure. We apply a relational fuzzy c-mean clustering approach to evaluating the performance of augmented session similarity measures in comparison with simple binary session similarity measures. The intra-cluster and inter-cluster distance based cluster quality ratio is used for performance evaluation. The results indicate that augmented session based intuitive augmented session (dis)similarity measure outperformed the other measures.
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