Summary
Current applications from industry, science, and business are storing huge amount of data everyday. This data most of the time comes from distributed sources and are usually analysed for the organizations to discover knowledge and recognize patterns by means of Data Mining (DM) techniques. This analysis usually requires to put all information together in a big centralized datasets. Analysing this huge dataset could be very expensive in terms of time and memory consuming. For reducing this cost some Distributed Data Mining (DDM) architectures have been developed in recently years. This paper presents an approach to building a distributed ID3 classifier which takes only metadata from distributed datasets avoiding the total access to the original data. This approach reduces the computing time nedeed to build the classifier.
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References
Witten, H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann Publishers, San Francisco (2005)
Talia, D., Trunfio, P., Verta, O.: Weka4WS: A WSRF-Enabled Weka Toolkit for Distributed Data Mining on Grids. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 309–320. Springer, Heidelberg (2005)
Khoussainov, R., Zuo, X., Kushmerick, N.: Grid-enabled Weka: A Toolkit for Machine Learning on the Grid. ERCIM 59, 47–48 (2004)
Shaikh Ali, A., Rana, O.F., Taylor, I.J.: Web Services Composition for Distributed Data Mining. In: International Conference Workshop on Parallel Processing, pp. 11–18. IEEE, Los Alamitos (2005)
Perez, M.S., Sanchez, A., Herrero, P., Robles, V.: Adapting the Weka Data Mining Toolkit to a Grid based environment. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 492–497. Springer, Heidelberg (2005)
Quinlan, J.R.: Induction of Decision Trees, Machine Learning, Hingham, MA, USA, vol. 1(1), pp. 81–106. Kluwer Academic Publishers, Dordrecht (1986)
Ross Quinlan, J.: C4.5: programs for machine learning. Morgan Kaufmann, San Francisco (1993)
McQueen, J.: Some methods for classification and analysis of multivariations. In: Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–2297 (1967)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2006)
University of Illinois and Data Mining Research Group and DAIS Research Laboratory, IlliMine 1.1.0, http://illimine.cs.uiuc.edu/
Statistics Department of the University of Auckland, R Project 2.6.1, http://www.r-project.org/
Williams, G.: Rattle 2.2.74, http://rattle.togaware.com/
Artificial Intelligence Unit of University of Dortmund, Yale 4.0, http://rapid-i.com/
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Jasso-Luna, O., Sosa-Sosa, V., Lopez-Arevalo, I. (2009). An Approach to Building a Distributed ID3 Classifier. In: Corchado, J.M., RodrÃguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_45
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DOI: https://doi.org/10.1007/978-3-540-85863-8_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85862-1
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