Abstract
To address the of mining a huge volume of geographically distributed databases, we propose two approaches. The first one is to download only a sample of each database. The second option is to mine each distributed database remotely and to download the resulting models to a central site and then aggregate these models. In this paper, we present an overview of the most common sampling techniques. We then present a new technique of distributed data-mining based on rule set models, where the aggregation technique is based on a confidence coefficient associated with each rule and on very small samples from each database. Finally, we present a comparison between the best sampling techniques that we found in the literature, and our approach of model aggregation.
This work is sponsored by NSERC.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aounallah, M., Mineau, G.: Rule Confidence Produced From Disjoint Databases: a Statistically Sound Way to Regroup Rules Sets. Accepted in IADIS international conference, Applied Computing (2004)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
John, G., Langley, P.: Static Versus Dynamic Sampling for Data Mining. In: Simoudis, E., Han, J., Fayya, U.M. (eds.) Proceedings of the Second International Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, August 1996, pp. 367–370. AAAI/MIT Press (1996)
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Proceedings of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval, Dublin, IE, pp. 3–12. Springer, Heidelberg (1994)
Mangasarian, O.L., Wolberg, W.H.: Cancer diagnosis via linear programming. SIAM News 23(5), 1–18 (1990)
Provost, F., Jensen, D., Oates, T.: Efficient Progressive Sampling. In: Chaudhuri, S., Madigan, D. (eds.) Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 15-18, pp. 23–32. ACM Press, New York (1999)
Ross Quinlan, J.: Improved Use of Continuous Attributes in C4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)
Saar-Tsechansky, M., Provost, F.: Active Sampling for Class Probability Estimation and Ranking (2001), http://www.mccombs.utexas.edu/faculty/Maytal.Saar-Tsechansky/home/MLJ-BootstrapLV-final.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aounallah, M., Quirion, S., Mineau, G.W. (2004). Distributed Data Mining vs. Sampling Techniques: A Comparison. In: Tawfik, A.Y., Goodwin, S.D. (eds) Advances in Artificial Intelligence. Canadian AI 2004. Lecture Notes in Computer Science(), vol 3060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24840-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-24840-8_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22004-6
Online ISBN: 978-3-540-24840-8
eBook Packages: Springer Book Archive