Abstract
Data mining is a relatively new field in the area of database research and artificial intelligence. Although it has appeared in research literature for only a few years, it is now one of the main topics of interest for research institutes and commercial laboratories. Conventional mathematical programming and statistics methods are used to perform data mining most often. In this paper we introduce the use of fuzzy set theory to combine a-priori expert knowledge and fuzzy techniques to extract rules with meaning to the user and in human language. We show that the complexity of the method outlined here is low, enabling us to explore large databases. We demonstrate our techniques on a real world data set and compare them with commercial software.
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Klein, Y., Pery, R., Komem, J., Kandel, A. (2000). Fuzzy Data Mining. In: Teodorescu, HN., Mlynek, D., Kandel, A., Zimmermann, HJ. (eds) Intelligent Systems and Interfaces. International Series in Intelligent Technologies, vol 15. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4401-2_5
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DOI: https://doi.org/10.1007/978-1-4615-4401-2_5
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