Abstract
In this work we are analyzing our ability to discover knowledge from multi-valued attributes (often referred in literature on fuzzy databases as collections [1-3]), that have been utilized in fuzzy relational database models [4-7] as a convenient way to represent uncertainty about the data recorded in the data tables. We present here implementation details and extended tests of a heuristic algorithm, which we used in the past [8-11] to interpret non-atomic values stored in fuzzy relational databases. In our evaluation we consider different data imprecision levels, as well as diverse shapes of fuzzy similarity hierarchies.
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Cubero, J.C., et al.: Fuzzy object Management in an Object-Relational Framework. In: Proc. of 10th Intl. Conf. of Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 1767–1774 (2004)
Ma, Z. (ed.): Advances In Fuzzy Object-oriented Databases: Modeling and Applications. Idea Group Publishing, Hershey (2004)
Berzal, F., et al.: Development of applications with fuzzy objects in modern programming platforms. International Journal of Intelligent Systems 20(11), 1117–1136 (2005)
Buckles, B.P., Petry, F.E.: A fuzzy representation of data for relational databases. Fuzzy Sets and Systems 7(3), 213–226 (1982)
Petry, F.E.: Fuzzy Databases: Principles and Applications. Kluwer Academic Publishers, Boston (1996)
Shenoi, S., Melton, A.: Proximity Relations in the Fuzzy Relational Database Model. International Journal of Fuzzy Sets and Systems 31(3), 285–296 (1989)
Shenoi, S., Melton, A., Fan, L.T.: Functional Dependencies and Normal Forms in the Fuzzy Relational Database Model. Information Sciences 60(1-2), 1–28 (1992)
Angryk, R.: Similarity-driven Defuzzification of Fuzzy Tuples for Entropy-based Data Classification Purposes. In: Proc. of 15th IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE ’06), Vancouver, Canada, July 2006, pp. 1490–1498. IEEE, Los Alamitos (2006)
Angryk, R.: On Interpretation of Non-Atomic Values and Induction of Decision Rules in Fuzzy Relational Databases. In: Rutkowski, L., et al. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 170–181. Springer, Heidelberg (2006)
Angryk, R., Petry, F., Ladner, R.: Mining Generalized Knowledge from Imperfect Data. In: Proc. of 10th Intl. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU ’04), Perugia, Italy, July 2004, pp. 739–746 (2004)
Angryk, R., Petry, F.: Discovery of Abstract Knowledge from Non-Atomic Attribute Values in Fuzzy Relational Databases. In: Bouchon-Meunier, B., Coletti, G., Yager, R. (eds.) Modern Information Processing, From Theory to Applications, Elsevier, pp. 171–182. Elsevier, Amsterdam (2006)
Kantardzic, M., Zurada, J.: New Generation of Data Mining Applications. IEEE Computer Society Press, Los Alamitos (2005)
Zadeh, L.A.: Similarity relations and fuzzy orderings. Information Sciences 3(2), 177–200 (1970)
Bock, H.–H., Diday, E. (eds.): Analysis of Symbolic Data, Exploratory Methods for Extracting Statistical Information from Complex Data. Springer, Heidelberg (2000)
Bertrand, P., Goupil, F.: Descriptive statistics for symbolic data. In: Bock, H.-H., Diday, E. (eds.) Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, pp. 103–124. Springer, Berlin (2000)
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Hossain, M.S., Angryk, R.A. (2007). Algorithm for Interpretation of Multi-valued Taxonomic Attributes in Similarity-Based Fuzzy Databases. In: Castillo, O., Melin, P., Ross, O.M., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72434-6_3
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DOI: https://doi.org/10.1007/978-3-540-72434-6_3
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