Advertisement

Comprehensiveness of Linguistic Data Summaries: A Crucial Role of Protoforms

  • Janusz Kacprzyk
  • Sławomir Zadrożny
Part of the Studies in Computational Intelligence book series (SCI, volume 445)

Abstract

We show first the essence of our approach to linguistic database summaries, equated with linguistically quantified propositions in Zadeh’s sense and mined through the use of a fuzzy querying interface to a database. We recast the problem from the perspective of comprehensiveness of patterns derived by linguistic data summaries. Motivated by Michalski’s [21] seminal approach to the comprehensiveness of data mining and machine learning results in which he advocates the use of natural language, we advocate the use of linguistic summaries which provide a new quality and an exceptional human consistency and comprehensiveness. We illustrate our analysis by two examples related to the linguistic summarization of both static and dynamic data in the area of analysis of innovativeness of companies and of Web server log files.

Keywords

Fuzzy Logic Association Rule Linguistic Term Data Mining Algorithm Truth Degree 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baczko, T., Kacprzyk, J., Zadrożny, S.: Towards knowledge driven individual integrated indicators of innovativeness. In: Jozefczyk, J., Orski, D. (eds.) Knowledge-Based Intelligent System Advancements: Systemic and Cybernetic Approaches, pp. 129–140. IGI Global, Hershey (2011)Google Scholar
  2. 2.
    Borgelt, C., Kruse, R.: Induction of association rules: Apriori implementation. In: Proc. 15th Conf. on Comp. Statistics (Compstat 2002), Berlin, Germany, pp. 395–400. Physica-Verlag, Heidelberg (2002)Google Scholar
  3. 3.
    Craven, M.W., Shavlik, J.W.: Extracting comprehensible concept representations from trained neural networks. In: Working Notes of the IJCAI 1995 Workshop on Comprehensibility in Machine Learning, Montreal, Canada, pp. 61–75 (1995)Google Scholar
  4. 4.
    Fisch, D., Gruber, T., Sick, B.: Swiftrule: Mining comprehensible classification rules for time series analysis. IEEE Transactions on Knowledge and Data Engineering 23(5), 774–787 (2011)CrossRefGoogle Scholar
  5. 5.
    George, R., Srikanth, R.: Data summarization using genetic algorithms and fuzzy logic. In: Herrera, F., Verdegay, J.L. (eds.) Genetic Algorithms and Soft Computing, pp. 599–611. Physica-Verlag, Heidelberg (1996)Google Scholar
  6. 6.
    Kacprzyk, J., Strykowski, P.: Linguistic summaries of sales data at a computer retailer: A case study. In: Proceedings of IFSA 1999, Taipei, Taiwan R.O.C, vol. 1, pp. 29–33 (1999)Google Scholar
  7. 7.
    Kacprzyk, J., Yager, R.R.: Linguistic summaries of data using fuzzy logic. International Journal of General Systems 30, 133–154 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Kacprzyk, J., Zadrożny, S.: FQUERY for Access: Fuzzy querying for a Windows-based DBMS. In: Bosc, P., Kacprzyk, J. (eds.) Fuzziness in Database Management Systems, pp. 415–433. Physica-Verlag, Heidelberg (1995)Google Scholar
  9. 9.
    Kacprzyk, J., Zadrożny, S.: Fuzzy queries in Microsoft Access v.2. In: Proc. FUZZ-IEEE/IFES 1995, Workshop on Fuzzy Database Systems and Information Retrieval, Yokohama, Japan, pp. 61–66 (1995)Google Scholar
  10. 10.
    Kacprzyk, J., Zadrożny, S.: On combining intelligent querying and data mining using fuzzy logic concepts. In: Bordogna, G., Pasi, G. (eds.) Recent Research Issues on the Management of Fuzziness in Databases, pp. 67–81. Physica-Verlag, Heidelberg (2000)Google Scholar
  11. 11.
    Kacprzyk, J., Zadrożny, S.: Computing with words in intelligent database querying: Standalone and internet-based applications. Information Sciences 134, 71–109 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Kacprzyk, J., Zadrożny, S.: Data mining via linguistic summaries of databases: An interactive approach. In: Ding, L. (ed.) A New Paradigm of Knowledge Engineering by Soft Computing, pp. 325–345. World Scientific, Singapore (2001)CrossRefGoogle Scholar
  13. 13.
    Kacprzyk, J., Zadrożny, S.: Fuzzy linguistic summaries via association rules. In: Kandel, A., Last, M., Bunke, H. (eds.) Data Mining and Computational Intelligence, pp. 115–139. Physica-Verlag, Heidelberg (2001)Google Scholar
  14. 14.
    Kacprzyk, J., Zadrożny, S.: Linguistic summarization of data sets using association rules. In: Proc. FUZZ-IEEE 2003, St. Louis, USA, pp. 702–707 (2003)Google Scholar
  15. 15.
    Kacprzyk, J., Zadrożny, S.: Linguistic database summaries and their protoforms: Towards natural language based knowledge discovery tools. Information Sciences 173(4), 281–304 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kacprzyk, J., Ziółkowski, A.: Database queries with fuzzy linguistic quantifiers. IEEE Transactions on Systems, Man and Cybernetics 16, 474–479 (1986)CrossRefGoogle Scholar
  17. 17.
    Kacprzyk, J., Zadrożny, S., Ziółkowski, A.: FQUERY III+: a ‘human consistent’ database querying system based on fuzzy logic with linguistic quantifiers. Information Systems 6, 443–453 (1989)CrossRefGoogle Scholar
  18. 18.
    Kacprzyk, J., Yager, R.R., Zadrożny, S.: A fuzzy logic based approach to linguistic summaries of databases. Int. Journal of Applied Mathematics and Computer Science 10, 813–834 (2001)Google Scholar
  19. 19.
    Kacprzyk, J., Wilbik, A., Zadrożny, S.: Linguistic summarization of time series using a fuzzy quantifier driven aggregation. Fuzzy Sets and Systems 159, 1485–1499 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Kacprzyk, J., Wilbik, A., Zadrożny, S.: An approach to the linguistic summarization of time series using a fuzzy quantifier driven aggregation. International Journal of Intelligent Systems 25(5), 411–439 (2010)zbMATHGoogle Scholar
  21. 21.
    Michalski, R.: A theory and methodology of inductive learning. Artificial Intelligence 20(2), 111–161 (1983)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Pryke, A., Beale, R.: Interactive Comprehensible Data Mining. In: Cai, Y. (ed.) Ambient Intelligence for Scientific Discovery. LNCS (LNAI), vol. 3345, pp. 48–65. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  23. 23.
    Wilbik, A., Kacprzyk, J.: Towards a multi-criteria analysis of linguistic summaries of time series via the measure of informativeness. International Journal of Data Analysis Techniques and Strategies 4(2), 181–204 (2012)CrossRefGoogle Scholar
  24. 24.
    Yager, R.R.: A new approach to the summarization of data. Information Sciences 28, 69–86 (1982)MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Yager, R.R.: On ordered weighted averaging operators in multicriteria decision making. IEEE Trans. on Systems, Man and Cybern 18(1), 183–190 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Yager, R.R., Kacprzyk, J.: The Ordered Weighted Averaging Operators: Theory and Applications. Kluwer, Boston (1997)CrossRefGoogle Scholar
  27. 27.
    Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Computers and Maths with Appls. 9, 149–184 (1983)MathSciNetzbMATHGoogle Scholar
  28. 28.
    Zadeh, L.A.: A prototype-centered approach to adding deduction capabilities to search engines — the concept of a protoform. BISC Seminar (2002)Google Scholar
  29. 29.
    Zadeh, L.A., Kacprzyk, J. (ed.): Computing with Words in Information/Intelligent Systems, 1. Foundations, 2. Applications. Physica-Verlag, Heidelberg (1999)Google Scholar
  30. 30.
    Zadrożny, S., Kacprzyk, J.: From a static to dynamic analysis of weblogs via linguistic summaries. In: Proceedings of 2011 IFSA World Congress and AFSS Congress, Surabaya, Indonesia (2011)Google Scholar
  31. 31.
    Zadrożny, S., De Tré, G., De Caluwe, R., Kacprzyk, J.: An overview of fuzzy approaches to flexible database querying. In: Galindo, J. (ed.) Handbook of Research on Fuzzy Information Processing in Databases, pp. 34–53. Idea Group, Inc. (2008)Google Scholar
  32. 32.
    Zhou, Z.H.: Comprehensibility of data mining algorithms. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, pp. 190–195. IGI Global, Hershey (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  2. 2.Warsaw School of Information TechnologyWarszawaPoland

Personalised recommendations