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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References
Christopher M. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995.
Juan A. Botia. Definition de un entorno distribuido para el Aprendizaje Computational Inductivo: la Arquitectura METALA. PhD thesis, Dpto. de Ingenieria de la Information y de las Comunicaciones, Universidad de Murcia, 2002.
Juan A. Botia, Antonio F. Skarmeta, Mercedes Garijo, and Juan R. Velasco. Handling a large number of machine learning experiments in a mas based system. In Workshop on Multi-Agent Systems Infraestructure and Scalability in Multi-agent Systems. Autonomous Agents Conference, Montreal, July 2001.
R. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems, 1(1), 1999.
R. Dubes and A. Jain. Algorithms that Cluster Data. Prentice Hall, Englewood Cliffs, 1988.
David A. Goldberg. Genetic Algorithms in search, optimization and machine learning. Addison-Wesley, 1989.
M. Holsheimer and A.P.J.M. Siebes. Data mining: the search for knowledge in databases. Technical Report CS-R9406 1994, Centrum voor Wiskunde en Informatica, Computer Science/Department of Algorithmics and Architecture, 1994.
Anupam Joshi and Raghu Krishnapuram. On mining web access logs. In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pages 63–69, 2000.
Raymond Kosala and Hendrik Blockeel. Web mining research: a survey. In SIGKDD Explorations. ACM, July 2000.
P. R. Krishnaiah and L. N. Kanal, editors. Classification, Pattern recognition and reduction of dimensionality, volume 2 of Handbook of Statistics. North Holland, Amsterdam, 1982.
Bamshad Mobasher, Robert Cooley, and Jaideep Srivastava. Automatic personalization based on Web usage mining. Communications of the ACM, 43(8):142–151, 2000.
O. Nasraoui, H. Frigui, A. Joshi, and R. Krishnapuram. Mining web access logs using relational competitive fuzzy clustering. August 1999.
John Foster Provost and Bruce G. Buchanan. Inductive policy: The pragmatics of bias selection. Machine Learning, 20:35, 1995.
J. Ross Quinlan. C4–5: Programs For Machine Learning. The Morgan Kaufmann series in Machine Learning. Morgan-Kauffman, San Mateo, California, 1993.
Jaideep Srivastava, Robert Cooley, Mukund Deshpande, and Pang-Ning Tan. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, l(2):12–23, 2000.
Ian H. Witten and Eibe Frank. Data Mining. Practical Machine Learning Tools and Techniques with JAVA Implementations. Morgan Kauffman, 2000.
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© 2003 Springer-Verlag Berlin Heidelberg
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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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