Advertisement

Using Alternative Contexts in Concept Hierarchies to Inspire Creativity

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 291)

Abstract

In this paper we examine issues involving measures of creativity for data generalization using hierarchies. In particular we consider consensus and specificity measures for the partitions that result using crisp concept hierarchies. We note that fuzzy hierarchies do not produce partitions of data in general so some approaches to considering “partitionness’ is described.

Keywords

creativity concept hierarchies congurence specificity partitions 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Boden, M.: The Creative Mind: Myths and Mechanisms. Weidenfield and Nicholson, London (1990)Google Scholar
  2. 2.
    Petry, F., Yager, R.: A Framework for Detecting Impending Critical Scenarios Using the Enhancement of Intelligence. Int. Jour. of Computational Intelligence Research 3(4), 287–295 (2007)Google Scholar
  3. 3.
    Yager, R., Petry, F.: A Multicriteria Approach to Data Summarization Using Concept Ontologies. IEEE Trans. on Fuzzy Syst. 14(6), 767–780 (2006)CrossRefGoogle Scholar
  4. 4.
    Pease, A., Winterstein, D., Colton, S.: Evaluating Machine Creativiity. In: Proc. of ICCBR 2001 – Workshop on Creative Systems, Vancouver, CA, pp. 56–61 (2001)Google Scholar
  5. 5.
    Ritchie, G.: Assessing Creativity. In: Proc. of AISB 2001 Symp. on AI and Creativity in Arts and Science, pp. 3–11 (2001)Google Scholar
  6. 6.
    Han, J., Cai, Y., Cercone, N.: Knowledge discovery in databases: An attribute-oriented approach. In: Proceedings of 18th VLDB Conf., pp. 547–559 (1992)Google Scholar
  7. 7.
    Han, J.: Mining Knowledge at Multiple Concept Levels. In: Proc. 4th Int’l Conf. on Information and Knowledge Management, pp. 19–24. ACM Press, New York (1995)Google Scholar
  8. 8.
    Yager, R.: On linguistic summaries of data. In: Piatesky-Shapiro, G., Frawley (eds.) Knowledge Discovery in Databases, pp. 347–363. MIT Press, Boston (1991)Google Scholar
  9. 9.
    Kacprzyk, J.: Fuzzy logic for linguistic summarization of databases. In: Proc. 8th Int’l Conf. on Fuzzy Systems, Seoul, Korea, pp. 813–818 (1999)Google Scholar
  10. 10.
    Dubois, D., Prade, H.: Fuzzy sets in data summaries - outline of a new approach. In: Proc. 8th Int’l Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Madrid, pp. 1035–1040 (2000)Google Scholar
  11. 11.
    Feng, L., Dillon, T.: Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses. IEEE Transactions on Knowledge and Data Engineering 15(1), 86–102 (2003)CrossRefGoogle Scholar
  12. 12.
    Lee, D., Kim, M.: Database summarization using fuzzy ISA hierarchies. IEEE Transactions on Systems, Man, and Cybernetics - Part B 27(1), 68–78 (1997)CrossRefGoogle Scholar
  13. 13.
    Raschia, G., Mouaddib, N.: SAINTETIQ:a fuzzy set-based approach to database summarization. Fuzzy Sets and Systems 129, 37–162 (2002)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Angryk, R., Petry, F.: Data Mining Fuzzy Databases Using Attribute-Oriented Generalization. In: Proc. IEEE Int. Conf. Data Mining Workshop on Foundations and New Directions in Data Mining, Melbourne, FL, pp. 8–15 (2003)Google Scholar
  15. 15.
    Cubero, J., Medina, J., Pons, O., Vila, M.: Data Summarization in Relational Databases Through Fuzzy Dependencies. Information Sciences 121(3-4), 233–270 (1999)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)MATHGoogle Scholar
  17. 17.
    Petry, F., Zhao, L.: Data Mining by Attribute Generalization with Fuzzy Hierarchies in Fuzzy Databases. Fuzzy Sets and Systems 160(15), 2206–2223 (2009)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Yager, R.: Some Measures Relating Partitions Useful for Computatuional Intellgience. Int. Jour. Computational Intelligence Systems 1(1), 1–18 (2008)CrossRefGoogle Scholar
  19. 19.
    Yager, R.R.: On Measures of Specificity. In: Kaynak, O., Zadeh, L.A., Turksen, B., Rudas, I.J. (eds.) Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, pp. 94–113. Springer, Berlin (1998)CrossRefGoogle Scholar
  20. 20.
    Klir, G.J.: Uncertainty and Information. John Wiley, New York (2006)Google Scholar
  21. 21.
    Yager, R.R.: Entropy and specificity in a mathematical theory of evidence. Int. J. of General Systems 9, 249–260 (1983)MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Yager, R.R.: Specificity measures of possibility distributions. In: Proceedings of the Tenth NAFIPS Meeting. U. of Missouri, Columbia, pp. 240–241 (1991)Google Scholar
  23. 23.
    Angryk, R., Petry, F.: Consistent Fuzzy Concept Hierarchies for Attribute Generalization. In: Proc. IASTED Int. Conf on Information and Knowledge Sharing, Scottsdale AZ, pp. 158–163 (2003)Google Scholar
  24. 24.
    Petry, F., Yager, R.: A Framework for Use of Imprecise Categorization in Developing Intelligent Systems. IEEE Trans. on Fuzzy Systems 18(2), 348–361 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.MC&G Code 7440.5, Naval Research Laboratory Stennis Space CenterHancock CountyUSA
  2. 2.Machine Intelligence InstituteIona CollegeNew RochelleUSA

Personalised recommendations