Abstract
Schema and record matching are tools to integrate files or databases. Record linkage is one of the tools used to link those records that while belonging to different files correspond to the same individual.
Standard record linkage methods are applied when the records of both files are described using the same variables. One of the non-standard record linkage methods corresponds to the case when files are not described using the same variables.
In this paper we study record linkage for non common variables. In particular, we use a supervised approach based on neural networks. We use a neural network to find the relationships between variables. Then, we use these relationships to translate the information in the domain of one file into the domain of the other file.
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
Agrawal, R., Srikant, R.: Privacy Preserving Data Mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, pp. 439–450 (2000)
Data Extraction System, U.S. Census Bureau, http://www.census.gov/DES/www/welcome.html
Freeman, J.A., Skapura, D.M.: Neural Networks. Algorithms Applications and Programming Techniques. Addison-Wesley, Reading (1991)
Li, W., Clifton, C.: SEMINT: A tool for identifying correspondences in heterogeneus databases using neural networks. Data & Knowledge Engineering 33, 49–84 (2000)
Narukawa, Y., Torra, V.: Twofold integral and Multi-step Choquet integral. Kybernetika 40(1), 39–50 (2004)
Narukawa, Y., Torra, V.: Graphical interpretation of the twofold integral and its generalization. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13(4), 415–424 (2005)
Nguyen, D., Widrow, B.: Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In: Proc. of the Int’l. Joint Conference on Neural Networks, vol. 3, pp. 21–26 (1990)
Nin, J., Torra, V.: Towards the use of OWA operators for record linkage. In: Proc. of the European Soc. on Fuzzy Logic and Technologies (in press, 2005)
Nin, J., Torra, V.: Empirical analysis of database privacy using twofold integrals. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 1–8. Springer, Heidelberg (2005)
Rojas, R.: Neural Networks - A Systematic Introduction. Springer, Heidelberg (1996)
Torra, V., Domingo-Ferrer, J.: Record linkage methods for multidatabase data mining. In: Torra, V. (ed.) Information Fusion in Data Mining, pp. 101–132. Springer, Heidelberg (2003)
Torra, V.: Towards the re-identification of individuals in data files with non-common variables. In: Proc. of the 14th European Conference on Artificial Intelligence (ECAI 2000), Berlin, Germany, pp. 326–330. IOS Press, Amsterdam (2000)
Torra, V.: OWA operators in data modeling and re-identification. IEEE Trans. on Fuzzy Systems 12(5), 652–660 (2004)
Murphy, P.M., Aha, D.W.: UCI Repository machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1994), http://www.ics.uci.edu/~mlearn/MLRepository.html
Willenborg, L., de Waal, T.: Elements of Statistical Disclosure Control. Lecture Notes in Statistics. Springer, Heidelberg (2001)
Winkler, W.E.: Data Cleaning Methods. In: Proc. SIGKDD 2003, Washington (2003)
Winkler, W.E.: Re-identification methods for masked microdata. In: Domingo-Ferrer, J., Torra, V. (eds.) PSD 2004. LNCS, vol. 3050, pp. 216–230. Springer, Heidelberg (2004)
Yager, R.R.: On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. Syst., Man, Cybern. 18, 183–190 (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nin, J., Torra, V. (2006). New Approach to the Re-identification Problem Using Neural Networks. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2006. Lecture Notes in Computer Science(), vol 3885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681960_25
Download citation
DOI: https://doi.org/10.1007/11681960_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32780-6
Online ISBN: 978-3-540-32781-3
eBook Packages: Computer ScienceComputer Science (R0)