Abstract
We present here Semantic and Descriptive Models for Classification as components of our Classification Model (definition [17]). We do so within a framework of a General Data Mining Model (definition [4]) which is a model for Data Mining viewed as a generalization process and sets standards for defining syntax and semantics and its relationship for any Data Mining method. In particular, we define the notion of truthfulness, or a degree of truthfulness of syntactic descriptions obtained by any classification algorithm, represented within the Semantic Classification Model by a classification operator. We use our framework to prove (theorems [1] and [3]) that for any classification operator (method, algorithm) the set of all discriminant rules that are fully true form semantically the lower approximation of the class they describe. The set of characteristic rules describes semantically its upper approximation. Similarly, the set of all discriminant rules for a given class that are partially true is semantically equivalent to approximate lower approximation of the class. The notion of the approximate lower approximation extends to any classification operator (method, algorithm) the ideas first expressed in 1986 by Wong, Ziarko, Ye [9] , and in the VPRS model of Ziarko [10].
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lin, T.Y.: Database Mining on Derived Attributes. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 14–32. Springer, Heidelberg (2002)
Menasalvas, E., Wasilewska, A., Fernández, C.: The lattice structure of the KDD process: Mathematical expression of the model and its operators. International Journal of Information Systems and Fundamenta Informaticae; special issue, 48–62 (2001)
Menasalvas, E., Wasilewska, A., Fernández-Baizan, M.C., Martinez, J.F.: Data Mining - A Semantical Model. In: Proceedings of 2002 World Congres on Computational Intelligence, Honolulu, Hawai, USA, May 11- 17, pp. 435–441 (2002)
Menasalvas, E., Wasilewska, A.: Data Mining as Generalization Process: A Formal MOdel. In: Foundation of Data Mining, Kluwer, Dordrecht (2005) (to appear)
Pawlak, Z.: Rough Sets - theoretical Aspects Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)
Wasilewska, A., Menasalvas, E., Fernández-Baizan, M.C.: Modelization of rough set functions in the KDD frame. In: 1st International Conference on Rough Sets and Current Trends in Computing (RSCTC 1998), June 22 - 26 (1998)
Wasilewska, A., Menasalvas, E.: Data Mining Operators. In: Proceedings of Foundations of Data Mining Workshop in Fourth IEEE International Conference on Data Mining, Brighton, UK, November 1-4 (2004)
Wasilewska, A., Menasalvas, E.: Data Preprocessing and Data Mining as Generalization Process. In: Proceedings of Foundations of Data Mining Workshop in Fourth IEEE International Conference on Data Mining, Brighton, UK, November 1-4 (2004)
Wong, S.K.M., Ziarko, W., Ye, R.L.: On Learning and Evaluation of Decision Rules in Context of Rough sets. In: Proceedings of the first ACM SIGART International Symposium on Methodologies for Intelligent Systems, Knoxville, Tenn, pp. 308–324 (1986)
Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and Systen Sciences 46(1), 39–59 (1993)
Yao, J.T., Yao, Y.Y.: Induction of Classification Rules by Granular Computing. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 331–338. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wasilewska, A., Menasalvas, E. (2005). A Classification Model: Syntax and Semantics for Classification. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_7
Download citation
DOI: https://doi.org/10.1007/11548706_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
Online ISBN: 978-3-540-31824-8
eBook Packages: Computer ScienceComputer Science (R0)