Semantics and Syntactic Patterns in Data

  • Eric Louie
  • Tsau Young Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)


This paper examines the semantics and syntactic views of classical association rule mining. A relational table is considered as a (knowledge) representation of a universe (= the set of real world entities). A pattern is said to be realizable, if there is a real world phenomenon corresponding to it. The central two issues are: Why do unrealizable data patterns appear? How could they be pruned away? For this purpose, the semantics of the original schema are considered. In additions, semantic is included into the knowledge representation of the universe. Based on model theory, two new relational structures, functions and binary relations, are added to represent some additional semantics of the given universe. Association rule mining based on such additional semantics are considered.


Data mining interesting-ness isomorphism semantics undirected association rules 


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  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proceeding of ACM-SIGMOD international Conference on Management of Data, Washington, DC, June 1993, pp. 207–216 (1993)Google Scholar
  2. 2.
    Fayad, U.M., Piatetsky-Sjapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: An overview. In: Fayard, U.M., Piatetsky-Sjapiro, G., Smyth, P., Uthurusamy (eds.) Knowledge Discovery in Databases, AAAI/MIT Press (1996)Google Scholar
  3. 3.
    Gracia-Molina, H., Ullman, J., Windin, J.: Database Systems The Complete Book. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  4. 4.
    Lin, T.Y.: Mining Un-interpreted Generalized Association Rules by Linear Inequalities: Deductive Data Mining Approach. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 204–212. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Lin, T.Y.: Attribute (Feature) Completion– The Theory of Attributes from Data Mining Prospect. In: The Proceedings of International Conference on Data Mining, Maebashi, Japan, December 9-12, pp. 282–289 (2002)Google Scholar
  6. 6.
    Lin, T.Y., Louie, E.: Semantics Oriented Association Rules. In: 2002 World Congress of Computational Intelligence, Honolulu, Hawaii, May 12-17, pp. 956–961 (2002) (paper # 5754)Google Scholar
  7. 7.
    Lin, T.Y., Yao, Y.Y., Louie, E.: Value Added Association Rules. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 328–333. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Lin, T.Y., Yao, Y.Y., Louie, E.: Value Added Association Rules. In: 6th Pacific-Asia Conference, Taipei, Taiwan, May 6-8 (2002)Google Scholar
  9. 9.
    Lin, T.Y., Louie, E.: Modeling the Real World for Data Mining: Granular Computing Approach. In: Joint 9th IFSA World Congress and 20th NAFIPS Conference, Vancouver, Canada, July 25-28 (2001)Google Scholar
  10. 10.
    Lin, T.Y.: Data Mining: Granular Computing Approach. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 24–33. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  11. 11.
    Lin, T.Y.: Granular Computing on Binary Relations I: Data Mining and Neighborhood Systems. In: Skoworn, A., Polkowski, L. (eds.) Rough Sets In Knowledge Discovery, pp. 107–121. Springer, Heidelberg (1998)Google Scholar
  12. 12.
    Lin, T.Y.: Database Mining on Derived Attributes–Granular and Rough Computing Approach. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 14–32. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Lin, T.Y., Louie, E.: Association Rules in Semantically Rich Relations. In: Terano, T., Nishida, T., Namatame, A., Tsumoto, S., Ohsawa, Y., Washio, T. (eds.) JSAI-WS 2001. LNCS (LNAI), vol. 2253, pp. 380–384. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Data Mining and Machine Oriented Modeling: A Granular Computing Approach. Journal of Applied Intelligence 13(2), Kluwer, 113–124 (September/October 2000)Google Scholar
  15. 15.
    Pei, Han, J., Lakshmanan, L.V.S.: Mining Frequent Itemsets with Convertible Constraints. In: Proc. 2001 Int. Conf. on Data Engineering (ICDE 2001), Heidelberg, Germany (April 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Eric Louie
    • 1
  • Tsau Young Lin
    • 2
  1. 1.IBM Almaden Research CenterSan JoseUSA
  2. 2.Department of Computer ScienceSan Jose State UniversitySan JoseUSA

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