C4.5: programs for machine learning
C4.5: programs for machine learning
Inductive Policy: The Pragmatics of Bias Selection
Machine Learning - Special issue on bias evaluation and selection
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Automatic personalization based on Web usage mining
Communications of the ACM
ACM SIGKDD Explorations Newsletter
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Data Mining: the search for knowledge in databases.
Data Mining: the search for knowledge in databases.
Knowledge mining with ELM system
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
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This work presents a methodological approach to build distributed information systems intended to work with inductive machine learning. More specifically, it introduces the METALA architecture. It is a set of recommendations which allows an user to work, generically, with that task. Web usage mining, using transactions clustering is used, as an example of possible applications of METALA. A methodological work path is followed to integrate not only the clustering algorithms but the produced models (i.e. centroids) from data. We demonstrate that a powerful web usage mining tool can be built by reusing a general purpose tool for inductive learning and with very little effort.