Abstract
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.
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Botia, J.A., Hernansaez, J.M., Gomez-Skarmeta, A. (2003). METAL A: a Distributed System for Web Usage Mining. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_89
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DOI: https://doi.org/10.1007/3-540-44869-1_89
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