Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Deductive Data Mining Using Granular Computing

  • Tsau Young LinEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_767


Decision rules, classification; Deductive data mining, model for automated data mining; Rough set theory, granular computing on partition

Definition of the Subject

What is deductive data mining (DDM)? It is a methodology that mathematically deduces patterns from the structure of the stored data. Among the three core techniques of data mining [1], classifications and association rule mining are deductive data mining. For clustering, its algorithms often use some properties of the ambient space, so we shall not include them. For this entry, we will focus on illustrating the idea on associations (association rules without “if-then”). Technically, we refer an association by the term a frequent itemset.

In relational database (RDB), a relation is a (time-varying) knowledge representation of a (time-varying) universe U of discourse (a set of entities) in terms of a (time-varying) set of attributes \({\mathcal {A}}=\{A_{1}, A_{2}, \ldots A_{n} \}\)

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Dunham M. Data mining introduction and advanced topics. Prentice Hall; 2003. ISBN:0-13-088892-3.Google Scholar
  2. 2.
    Fayad UM, Piatetsky-Sjapiro G, Smyth P. From data mining to knowledge discovery: an overview. In: Fayard UM, Piatetsky-Sjapiro G, Smyth P, Uthurusamy R, editors. Knowledge discovery in databases. AAAI/MIT Press; Boston; 1996.Google Scholar
  3. 3.
    Ginsburg S. Richard Hull. Ordered attribute domains in the relational model. In: Proceedings of the XP2 Workshop on Relational Database Theory; 1981.Google Scholar
  4. 4.
    Ginsburg S. Richard Hull. Order dependency in the relational model. Theor Comput Sci. 1983;26(1–2):149–95.CrossRefzbMATHGoogle Scholar
  5. 5.
    Gracia-Molina H, Ullman J, Windin J. Database systems the complete book. New Jersey: Prentice Hall; 2002.Google Scholar
  6. 6.
    Hsiao DK, Harary F. A formal system for information retrieval from files. Commun ACM. 1970;13(2): 67-73. Corrigenda 1970;13(4).CrossRefzbMATHGoogle Scholar
  7. 7.
    Lee TT. Algebraic theory of relational databases. Bell Syst Tech J. 1983;62(10):3159–204.CrossRefMathSciNetzbMATHGoogle Scholar
  8. 8.
    Lin TY. Neighborhood systems and relational database. In: Proceedings of the 15th ACM Annual Conference on Computer Science; 1988. p. 725.Google Scholar
  9. 9.
    Lin TY. Neighborhood systems and approximation in database and knowledge base systems. In: Proceedings of the 4th International Symposium on Methodologies of Intelligent Systems; 1989. p. 75–86.Google Scholar
  10. 10.
    Lin TY. Chinese wall security policy – an aggressive model. In: Proceedings of the 5th Aerospace Computer Security Application Conference; 1989. p. 286–93.Google Scholar
  11. 11.
    Lin TY. Topological and fuzzy rough sets. In: Slowinski R, editor. Decision support by experience – application of the rough sets theory. Kluwer Academic Publishers; Heiderberg: New York; 1992. p. 287–304.CrossRefGoogle Scholar
  12. 12.
    Lin TY. Granular computing on binary relations: I. Da mining and neighborhood systems. I, II: rough set representations and belief functions. In: Skoworn A, Polkowski L, editors. Rough sets in knowledge discovery. Physica-Verlag; Heiderberg: New York; 1998. p. 107-121–140.Google Scholar
  13. 13.
    Lin TY. Data mining and machine oriented modeling: a granular computing approach. Appl Intell. 2000;13(2):113–24.CrossRefGoogle Scholar
  14. 14.
    Lin TY. Chinese wall security policy models: information flows and confining Trojan Horses. In: Proceedings of the 17th Annual IFIP WG11.3 Working Conference on Database Security; 2003. p. 275–87.CrossRefGoogle Scholar
  15. 15.
    Lin TY. Attribute (Feature) completion – the theory of attributes from data mining prospect. In: Proceedings of the 2nd IEEE International Conference on Data Mining; 2002. p. 282–9.Google Scholar
  16. 16.
    Lin TY. Mining associations by linear inequalities. In: Proceedings of the 4th IEEE International Conference on Data Mining; 2004. p. 154–61.Google Scholar
  17. 17.
    Lin TY. Granular computing: examples, intuitions and modeling. In: Proceedings of the IEEE International Conference on Granular Computing; 2005. p. 40–4.Google Scholar
  18. 18.
    Lin TY, Chiang IJ. A simplicial complex, a hypergraph, structure in the latent semantic space of document clustering. Int J Approx Reason. 2005;40(1–2):55–80.CrossRefMathSciNetzbMATHGoogle Scholar
  19. 19.
    Lin TY. A roadmap from rough set theory to granular computing. In: Proceedings of the 1st International Conference on Rough Sets and Knowledge Technology; 2006. p. 33–41.CrossRefGoogle Scholar
  20. 20.
    Louie E, Lin TY. Finding association rules using fast bit computation: machine-oriented modeling. In: Proceedings of the 12th International Symposium on Methodologies for Intelligent Systems; 2000. p. 486–94.Google Scholar
  21. 21.
    Pawlak Z. Rough sets. Theoretical aspects of reasoning about data. Boston: Kluwer Academic Publishers; 1991.zbMATHGoogle Scholar
  22. 22.
    Zadeh LA. Fuzzy sets information and control. 1965;8(3):338–53.CrossRefMathSciNetzbMATHGoogle Scholar
  23. 23.
    Zadeh LA. Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/ intelligent systems. Soft Comput. 1998;2(1):23–5.CrossRefGoogle Scholar
  24. 24.
    Wong E, Chiang TC. Canonical structure in attribute based file organization. Commun ACM. 1971;14(9):593–7.CrossRefzbMATHGoogle Scholar
  25. 25.
    Hector Garcia-Molina, Jeffrey D. Ullman, Jennifer Widom: Database systems - the complete book (2. ed.). Pearson Education 2009, ISBN 978-0-13-187325-4, p. I–XXVI, 1–1203Google Scholar
  26. 26.
    Tsau Young Lin. Granular Computing: Practices, Theories, and Future Directions. In: Encyclopedia of Complexity and Systems Science. 2009. p. 4339–4355.Google Scholar
  27. 27.
    Tsau Young Lin. Very fast frequent itemset mining: Simplicial complex methods (Extended abstract). BigData 2016. p. 1946–194.Google Scholar
  28. 28.
    Tsau Young Lin. Homology group of Frequent Itemsets (Extended Abstract). BigData 2017. New 5.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceSan Jose State UniversitySan JoseUSA