A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering validity checking methods: part II
ACM SIGMOD Record
An Efficient Technique for Mining Usage Profiles Using Relational Fuzzy Subtractive Clustering
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
LODAP: a log data preprocessor for mining web browsing patterns
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
An overview of web data clustering practices
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
A System for Deriving a Neuro-Fuzzy Recommendation Model
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
A fuzzy bi-clustering approach to correlate web users and pages
International Journal of Knowledge and Web Intelligence
NEWER: A system for NEuro-fuzzy WEb Recommendation
Applied Soft Computing
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
Journal of Systems and Software
Hi-index | 0.00 |
User profiling is a fundamental task in Web personalization. Fuzzy clustering is a valid approach to derive user profiles by capturing similar user interests from web usage data available in log files. Often, fuzzy clustering is based on the assumption that data lay on an Euclidean space; however, clustering based on Euclidean distance can lead the clustering process to find user representations that do not capture the semantic information incorporated in the original Web usage data. In this paper, we propose a different approach to express similarity between Web users. The measure is based on the evaluation of similarity between fuzzy sets. The proposed measure is employed in a relational fuzzy clustering algorithm to discover clusters embedded in the Web usage data and derive profiles modeling the real user preferences. An application example on usage data extracted from logfiles of a sample Web site is reported and a comparison with the results obtained using the cosine measure is shown to demonstrate the effectiveness of the proposed similarity measure.