Abstract
We present rminer, our open source library for the R tool that facilitates the use of data mining (DM) algorithms, such as neural Networks (NNs) and support vector machines (SVMs), in classification and regression tasks. Tutorial examples with real-world problems (i.e. satellite image analysis and prediction of car prices) were used to demonstrate the rminer capabilities and NN/SVM advantages. Additional experiments were also held to test the rminer predictive capabilities, revealing competitive performances.
This work is supported by FCT grant PTDC/EIA/64541/2006.
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
Asuncion, A., Newman, D.: UCI Machine Learning Repository, Univ. of California, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0: Step-by-step data mining guide. CRISP-DM consortium (2000)
Cherkassy, V., Ma, Y.: Practical Selection of SVM Parameters and Noise Estimation for SVM Regression. Neural Networks 17(1), 113–126 (2004)
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., Reis, J.: Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems 47(4), 547–553 (2009)
Cortez, P., Lopes, C., Sousa, P., Rocha, M., Rio, M.: Symbiotic Data Mining for Personalized Spam Filtering. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI 2009), pp. 149–156. IEEE, Los Alamitos (2009)
Cortez, P., Teixeira, J., Cerdeira, A., Almeida, F., Matos, T., Reis, J.: Using data mining for wine quality assessment. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 66–79. Springer, Heidelberg (2009)
Goebel, M., Gruenwald, L.: A Survey of Data Mining and Knowledge Discovery Software Tools. SIGKDD Explorations 1(1), 20–33 (1999)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, NY (2008)
Kewley, R., Embrechts, M., Breneman, C.: Data Strip Mining for the Virtual Design of Pharmaceuticals with Neural Networks. IEEE Trans. Neural Networks 11(3), 668–679 (2000)
Piatetsky-Shapiro, G.: Data Mining Tools Used Poll (2009), http://www.kdnuggets.com/polls/2009/data-mining-tools-used.htm
Provost, F., Domingos, P.: Tree Induction for Probability-Based Ranking. Machine Learning 52(3), 199–215 (2003)
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2009), ISBN 3-900051-00-3 http://www.R-project.org
Rexer, K.: Second annual data miner survey. Technical report, Rexer Analytics (2008)
Rocha, M., Cortez, P., Neves, J.: Evolution of Neural Networks for Classification and Regression. Neurocomputing 70, 2809–2816 (2007)
Tinoco, J., Correia, A.G., Cortez, P.: A Data Mining Approach for Jet Grouting Uniaxial Compressive Strength Prediction. In: World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), Coimbatore, India, December 2009, pp. 553–558. IEEE, Los Alamitos (2009)
Turban, E., Sharda, R., Aronson, J., King, D.: Business Intelligence, A Managerial Approach. Prentice-Hall, Englewood Cliffs (2007)
Williams, G.: Rattle: A Data Mining GUI for R. The R Journal 1(2), 45–55 (2009)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. The Journal of Machine Learning Research 5, 975–1005 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cortez, P. (2010). Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-14400-4_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14399-1
Online ISBN: 978-3-642-14400-4
eBook Packages: Computer ScienceComputer Science (R0)