Previous |  Up |  Next


Title: On a fuzzy querying and data mining interface (English)
Author: Kacprzyk, Janusz
Author: Zadrożny, Sławomir
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 36
Issue: 6
Year: 2000
Pages: [657]-670
Summary lang: English
Category: math
Summary: In the paper an interface is proposed that combines flexible (fuzzy) querying and data mining functionality. The point of departure is the fuzzy querying interface designed and implemented previously by the present authors. It makes it possible to formulate and execute, against a traditional (crisp) database, queries containing imprecisely specified conditions. Here we discuss possibilities to extend it with some data mining features. More specifically, linguistic summarization of data (databases), as introduced by Yager [Yager91], is advocated as an interesting extension of simple querying. The link between linguistic (fuzzy) data summaries and association rules is discussed and exploited. (English)
Keyword: data mining inference
Keyword: linguistic (fuzzy) data
MSC: 68P05
MSC: 68P20
MSC: 68T30
MSC: 68T37
idZBL: Zbl 1249.68256
Date available: 2009-09-24T19:36:06Z
Last updated: 2015-03-27
Stable URL:
Reference: [1] Agrawal R., Srikant R.: Fast algorithms for mining association rules.In: Proc. 20th Internat. Conference on Very Large Databases, Santiago 1994
Reference: [2] Anwar T. M., Beck H. W., Navathe S. B.: Knowledge mining by imprecise querying: A classification based system.In: Proc. Internat. Conference on Data Engineering, Tampa, USA 1992, pp. 622–630
Reference: [3] George R., Srikanth R.: Data summarization using genetic algorithms and fuzzy logic.In: Genetic Algorithms and Soft Computing (F. Herrera and J. L. Verdegay, eds.), Physica–Verlag, Heidelberg – New York 1996, pp. 599–611
Reference: [4] Kacprzyk J., Strykowski P.: Linguistic data summaries for intelligent decision support.In: Fuzzy Decision Analysis and Recognition Technology for Management, Planning and Optimization – Proceedings of EFDAN’99 (R. Felix, ed.), Germany 1999, pp. 3–12
Reference: [5] Kacprzyk J., Strykowski P.: Linguistic Summaries of Sales Data at a Computer Retailer: A Case Study.In: Proceedings of IFSA’99 (Taipei, Taiwan R.O.C), vol. 1, 1999, pp. 29–33
Reference: [6] Kacprzyk J., Zadrożny S.: FQUERY for Access: fuzzy querying for a Windows-based DBMS.In: Fuzziness in Database Management Systems (P. Bosc and J. Kacprzyk, eds.), Physica–Verlag, Heidelberg 1995, pp. 415–433
Reference: [7] Kacprzyk J., Zadrożny S.: Flexible querying using fuzzy logic: An implementation for Microsoft Access.In: Flexible Query Answering Systems (T. Andreasen, H. Christiansen and H. L. Larsen, eds.), Kluwer, Boston 1997, pp. 247–275 Zbl 0886.68061
Reference: [8] Kacprzyk J., Zadrożny S.: Data mining via linguistic summaries of data: An interactive approach.In: Methodologies for the Conception, Design and Application of Soft Computing (T. Yamakawa and G. Matsumoto, eds., Proceedings of IIZUKA’98), Iizuka 1998, pp. 668–671
Reference: [9] Kacprzyk J., Zadrożny S.: On summarization of large datasets via a fuzzy-logic-based querying add-on to Microsoft Access.In: Intelligent Information Systems VII, Malbork, IPI PAN, Warsaw 1998, pp. 249–258
Reference: [10] Lee J.-H., Lee–Kwang H.: An extension of association rules using fuzzy sets.In: Proc. Seventh IFSA World Congress, Prague 1997, Vol. 1, pp. 399–402
Reference: [11] Liu B., Hsu W., Yiming M.: Integrating Classification and Association Rule Mining.In: Proc. Fourth Internat. Conference on Knowledge Discovery and Data Mining (KDD-98, Plenary Presentation), New York 1998
Reference: [12] Mannila H., Toivonen H., Verkamo A. I.: Efficient algorithms for discovering association rules.In: Proc. AAAI Workshop on Knowledge Discovery in Databases (U. M. Fayyad and R. Uthurusamy, eds.), Seattle 1994, pp. 181–192
Reference: [13] Srikant R., Agrawal R.: Mining generalized association rules.In: Proc. 21st Internat. Conference on Very Large Databases, Zurich 1995
Reference: [14] Srikant R., Agrawal R.: Mining quantitative association rules in large relational tables.In: Proc. ACM–SIGMOD 1996 Conference on Management of Data, Montreal 1996
Reference: [15] Srikant R., Vu Q., Agrawal R.: Mining association rules with item constraints.In: Proc. 3rd Internat. Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach 1997
Reference: [16] Yager R. R.: On linguistic summaries of data.In: Knowledge Discovery in Databases (G. Piatetsky–Shapiro and W. J. Frawley, eds.), AAAI Press/The MIT Press, Menlo Park 1991, pp. 347–363
Reference: [17] Zadeh L. A.: A computational approach to fuzzy quantifiers in natural languages.Comput. Math. Appl. 9 (1983), 149–184 Zbl 0517.94028, MR 0719073, 10.1016/0898-1221(83)90013-5
Reference: [18] Zadeh L. A.: A computational theory of dispositions.Internat. J. Intelligent Systems 2 (1987), 39–64 Zbl 0641.68153


Files Size Format View
Kybernetika_36-2000-6_4.pdf 2.505Mb application/pdf View/Open
Back to standard record
Partner of
EuDML logo