Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
From user access patterns to dynamic hypertext linking
Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
The World-Wide Web: quagmire or gold mine?
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Database techniques for the World-Wide Web: a survey
ACM SIGMOD Record
Adaptive Web sites: automatically synthesizing Web pages
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Automatic resource compilation by analyzing hyperlink structure and associated text
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Using path profiles to predict HTTP requests
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Discovering similar patterns in time series
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
A fine grained heuristic to capture web navigation patterns
ACM SIGKDD Explorations Newsletter
Exploring the Web with reconnaissance agents
Communications of the ACM
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
INSITE: A Tool for Interpreting Users? Interaction with a Web Space
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Web Browser Intelligence: Opening Up the Web
COMPCON '97 Proceedings of the 42nd IEEE International Computer Conference
Knowledge discovery from users Web-page navigation
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Explosive growth in size and usage of the World Wide Web has made it Necessary for Web site administrators to track and analyze the navigation patterns of Web site visitors. However, data mining techniques are not easily applicable to Web data due to problems both related with the technology underlying the Web and the lack of standards in the design and implementation of Web pages. Information collected by Web servers and kept in the server log is the main source of data for analyzing user navigation patterns.Once logs have been preprocessed and sessions have been obtained there are several kinds of access pattern mining that can be performed depending on the needs of the analyst. It is important to mention that most efforts have relied on relatively simple techniques which can be inadequate for real user profile data since noise in the data has to be firstly tacked. Thus, there is a need for robust methods that integrates different intelligent techniques that are free of any assumptions about the noise contamination rate.In this paper, the problem of mining behavior patterns on the Web is studied: in detail and different approaches to solve the problem are analyzed. An algorithm is given to calculate frequent access patterns. This algorithm is based on a model structure that has been called WPC-Tree that stores in each node relevant information about pages that make it possible to apply data mining techniques to obtain useful patterns.