Abstract
This chapter reviews the main uses of information fusion techniques in the field of data mining. A classification of these uses is given into three rough classes: preprocessing, building models and information extraction.
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
Bauer, E., Kohavi, R., (1999), An Empiricial Comparison of Voting Classification Algorithms: Bagging, Boosting and Variants, Machine Learning 36 105–139.
Breiman, L., (1996), Bagging predictors, Machine Learning, 24 123–140.
Cox, T. F., Cox, M. A. A., (1994), Multidimensional scaling, Chapman and Hall.
Detyniecki, M., (2000), Mathematical Aggregation Operators and their Application to Video Querying, PhD dissertation, University of Paris VI, Paris, France.
Doyle, P., Lane, J. I., Theeuwes, J. J. M., Zayatz, L. M., (Eds.), (2001), Confidentiality, Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, Elsevier.
Godo, L., Torra, V., (2000), On aggregation operators for ordinal qualitative information, IEEE T. on Fuzzy Systems, 8:143–154.
Grabisch, M., (2000), Fuzzy integral for classification and feature extraction, in M. Grabisch, T. Murofushi and M. Sugeno (Eds), Fuzzy Measures and Integrals, Physica-Verlag, 415–434.
Hastie, T., Tibshirani, R., Friedman, J., (2001), The Elements of Statistical Learning, Berlin: Springer.
Huang, Z., Ng, M. K., (1999), A fuzzy k-modes algorithm for clustering categorical data, IEEE Trans. on Fuzzy Systems, 7:4 446–452.
http://www.integrity.com
Ishibuchi, H., Morisawa, T., Nakashima, T., (1996), Voting Schemes for fuzzyrule-based classification systems, Proc. of the Sixth IEEE Int. Conference on Fuzzy Systems, 614–620, Barcelona, Catalonia, Spain.
Kohonen, T., (1997), Self-Organizing maps, 2nd edition, Springer-Verlag.
Luo, R.C., Kay, M.G., (1992), Data fusion and sensor integration: State-of-the-art 1990s, in M. Al Abidi, R. C. Gonzalez, (Eds.), Data Fusion in Robotics and Machine Intelligence, Academic Press, 7–135.
Merz, C. J., (1999), Using Correspondence Analysis to Combine Classifiers, Machine Learning, 36 33–58.
Merz, C. J., Pazzani, M. J., (1999), Combining regression estimates, Machine learning, 36 9–32.
Schapire, R. E., (1990), The strength of weak learnability, Machine learning, 5:2 197–227.
Torra, V., (1999), On Some Relationships between Hierarchies of Quasiarithmetic Means and Neural Networks, Int. J. of Intel. Syst. 14:11 1089–1098.
Torra, V., (2000), Towards the re-identification of individuals in data files with non-common variables, Proc. of the European Conf. on Artificial Intelligence (ECAI 2000), 326–330, Berlin, Germany.
Torra, V., (2003), Information Fusion in Data Mining: Outline, Chapter in this book.
Webb, G.I., (2000), Multi Boosting: A Technique for Combining Boosting and Wagging, Machine Learning, 40 159–196.
Winkler, W. E., (1995), Advanced methods for record linkage, American Statistical Association, Proc. of the Section on Survey Research Methods, 467–472.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Torra, V. (2003). Trends in Information fusion in Data Mining. In: Torra, V. (eds) Information Fusion in Data Mining. Studies in Fuzziness and Soft Computing, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36519-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-540-36519-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05628-4
Online ISBN: 978-3-540-36519-8
eBook Packages: Springer Book Archive