Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis
INFORMS Journal on Computing
Mining Navigation Patterns Using a Sequence Alignment Method
Knowledge and Information Systems
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
MASON: A Multiagent Simulation Environment
Simulation
A Novel Approach for Determination of Optimal Number of Cluster
ICCAE '09 Proceedings of the 2009 International Conference on Computer and Automation Engineering
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
Website reorganization using an ant colony system
Expert Systems with Applications: An International Journal
Stochastic Simulation of Web Users
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Advanced Techniques in Web Intelligence -1
Advanced Techniques in Web Intelligence -1
On how ants put advertisements on the web
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
A web browsing cognitive model
KES'12 Proceedings of the 16th international conference on Knowledge Engineering, Machine Learning and Lattice Computing with Applications
A neurology-inspired model of web usage
Neurocomputing
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An ant colony optimization-based algorithm to predict web usage patterns is presented. Our methodology incorporates multiple data sources, such as web content and structure, as well as web usage. The model is based on a continuous learning strategy based on previous usage in which artificial ants try to fit their sessions with real usage through the modification of a text preference vector. Subsequently, trained ants are released onto a new web graph and the new artificial sessions are compared with real sessions, previously captured via web log processing. The main results of this work are related to an effective prediction of the aggregated patterns of real usage, reaching approximately 80%. In the second place, this approach allows the obtaining of a quantitative representation of the keywords that influence the navigational sessions.