Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A Hybrid Approach to Web Usage Mining
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Creating Adaptive Web Sites Through Usage-Based Clustering of URLs
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
Web Usage Mining Based on Clustering of Browsing Features
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
Particle Swarm Optimization Based Clustering of Web Usage Data
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Computational intelligence-based personalization of interactive web systems
WSEAS Transactions on Information Science and Applications
Model-Based cluster analysis for web users sessions
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
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Biclustering has the potential to make significant contributions in the fields of information retrieval, web mining, and so forth. In this paper, the authors analyze the complex association between users and pages of a web site by using a biclustering algorithm. This method automatically identifies the groups of users that show similar browsing patterns under a specific subset of the pages. In this paper, mutation operator from Genetic Algorithms is incorporated into the Binary Particle Swarm Optimization (BPSO) for biclustering of web usage data. This hybridization can increase the diversity of the population and help the particles effectively escape from the local optimum. It detects optimized user profile group according to coherent browsing behavior. Experiments are performed on a benchmark clickstream dataset to test the effectiveness of the proposed algorithm. The results show that the proposed algorithm has higher performance than existing PSO methods. The interpretation of this biclustering results are useful for marketing and sales strategies.