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Logics for Representing Data Mining Tasks in Inductive Databases

  • Hong-Cheu Liu
  • Millist Vincent
  • Jixue Liu
  • Jiuyong Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8506)

Abstract

We present a logical framework for querying inductive databases, which can accommodate a variety of data mining tasks, such as classification, clustering, finding frequent patterns and outliers detection. We also address the important issues of the expressive power of inductive query languages. We show that the proposed logic programming paradigm has equivalent expressive power to an algebra for data mining presented in the literature [1].

Keywords

Data Mining Association Rule Logic Programming Frequent Itemsets Expressive Power 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Liu, H.-C., Ghose, A., Zeleznikow, J.: Towards an algebraic framework for querying inductive databases. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010, Part II. LNCS, vol. 5982, pp. 306–312. Springer, Heidelberg (2010)Google Scholar
  2. 2.
    Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Communications of the ACM 39(11), 58–64 (1996)CrossRefGoogle Scholar
  3. 3.
    Džeroski, S.: Inductive databases and constraint-based data mining. In: Jäschke, R. (ed.) ICFCA 2011. LNCS (LNAI), vol. 6628, pp. 1–17. Springer, Heidelberg (2011)Google Scholar
  4. 4.
    Romei, A., Turini, F.: Inductive databases languages: Requirements and examples. Knowledge Information Systems 26, 351–384 (2011)CrossRefGoogle Scholar
  5. 5.
    Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2000)Google Scholar
  6. 6.
    Calders, T., Lakshmanan, L., Ng, R., Paredaens, J.: Expressive power of an algebra for data mining. ACM Transactions on Database Systems 31(4), 1169–1214 (2006)CrossRefGoogle Scholar
  7. 7.
    Giannotti, F., Manco, G., Turini, F.: Towards a logic query language for data mining. In: Meo, R., Lanzi, P.L., Klemettinen, M. (eds.) Database Support for Data Mining Applications. LNCS (LNAI), vol. 2682, pp. 76–94. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Raedt, L.D.: A perspective on inductive databases. SIGKDD Explorations 4(2), 69–77 (2002)CrossRefGoogle Scholar
  9. 9.
    Mayer-Schonberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Eamon Dolan/Houghton Mifflin Harcourt (2013)Google Scholar
  10. 10.
    Han, J., Fu, Y., Koperski, K., Wang, W., Zaiane, O.: Dmql: A data mining query language for relational databases. In: Proceedings of ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (1996)Google Scholar
  11. 11.
    Meo, R., Psaila, G., Ceri, S.: An extension to sql for mining association rules. Data Mining and Knowledge Discovery 2(2), 195–224 (1998)CrossRefGoogle Scholar
  12. 12.
    Nijssen, S., De Raedt, L.: Iql: A proposal for an inductive query language. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 189–207. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Angiulli, F., Ben-Eliyahu-Zohary, R., Palopoli, L.: Outlier detection using default reasoning. Artificial Intelligence, Elsevier 172, 1837–1872 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Reiter, R.: A logic for default reasoning. Artificial Intelligence 13(1-2), 81–132 (1980)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hong-Cheu Liu
    • 1
  • Millist Vincent
    • 1
  • Jixue Liu
    • 1
  • Jiuyong Li
    • 1
  1. 1.School of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaideAustralia

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