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Abstract

In this paper we propose a new type of protoform-based linguistic summary – the gradual summary. This new type of summaries aims in capturing the change over some time span. Such summaries can be useful in many domains, for instance in economics, e.g., “prices of X are getting smaller”, in eldercare, e.g., “resident Y is getting less active”, in managing production, e.g. “production is dropping” or “delays in deliveries are getting smaller”.

Keywords

linguistic summaries fuzzy logic computing with words protoforms 

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References

  1. 1.
    Bosc, P., Dubois, D., Pivert, O., Prade, H., Calmes, M.D.: Fuzzy summarization of data using fuzzy cardinalities. In: Proceedings of the IPMU 2002 Conference, pp. 1553–1559 (2002)Google Scholar
  2. 2.
    Castillo-Ortega, R., Marín, N., Sánchez, D.: Time series comparison using linguistic fuzzy techniques. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. LNCS, vol. 6178, pp. 330–339. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Castillo-Ortega, R., Marín, N., Sánchez, D.: Linguistic local change comparison of time series. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 2909–2915 (June 2011)Google Scholar
  4. 4.
    Dubois, D., Prade, H.: Gradual rules in approximate reasoning. Information Sciences 61, 103–122 (1992)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Kacprzyk, J.: Intelligent data analysis via linguistic data summaries: a fuzzy logic approach. In: Decker, R., Gaul, W. (eds.) Classification and Information Processing at the Turn of Millennium, pp. 153–161. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Kacprzyk, J., Fedrizzi, M.: “Soft” consensus measures for monitoring real consensus reaching processes under fuzzy preferences. Control and Cybernetics 15, 309–323 (1986)Google Scholar
  7. 7.
    Kacprzyk, J., Fedrizzi, M.: A ’human-consistent‘ degree of consensus based on fuzzy logic with linguistic quantifiers. Mathematical Social Sciences 18, 275–290 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Kacprzyk, J., Strykowski, P.: Linguistic data summaries for intelligent decision support. In: Felix, R. (ed.) Proceedings of EFDAN 1999-4th European Workshop on Fuzzy Decision Analysis and Recognition Technology for Management, pp. 3–12 (1999)Google Scholar
  9. 9.
    Kacprzyk, J., Strykowski, P.: Linguistic summaries of sales data at a computer retailer: a case study. In: Proceedings of IFSA 1999, vol. 1, pp. 29–33 (1999)Google Scholar
  10. 10.
    Kacprzyk, J., Wilbik, A., Zadrożny, S.: Linguistic summarization of time series using a fuzzy quantifier driven aggregation. Fuzzy Sets and Systems 159(12), 1485–1499 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    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
  12. 12.
    Kacprzyk, J., Yager, R.R.: Linguistic summaries of data using fuzzy logic. International Journal of General Systems 30, 33–154 (2001)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Kacprzyk, J., Yager, R.R., Zadrożny, S.: A fuzzy logic based approach to linguistic summaries of databases. International Journal of Applied Mathematics and Computer Science 10, 813–834 (2000)zbMATHGoogle Scholar
  14. 14.
    Kacprzyk, J., Zadrożny, S.: Fuzzy linguistic data summaries as a human consistent, user adaptable solution to data mining. In: Gabrys, B., Leiviska, K., Strackeljan, J. (eds.) Do Smart Adaptive Systems Exist? STUDFUZZ, vol. 173, pp. 321–340. Springer, New York (2005)CrossRefGoogle Scholar
  15. 15.
    Kacprzyk, J., Zadrożny, S.: Linguistic database summaries and their protoforms: toward natural language based knowledge discovery tools. Information Sciences 173, 281–304 (2005)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Raschia, G., Mouaddib, N.: SAINTETIQ: a fuzzy set-based approach to database summarization. Fuzzy Sets and Systems 129, 137–162 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Rasmussen, D., Yager, R.R.: Finding fuzzy and gradual functional dependencies with SummarySQL. Fuzzy Sets and Systems 106, 131–142 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Ros, M., Pegalajar, M., Delgado, M., Vila, A., Anderson, D.T., Keller, J.M., Popescu, M.: Linguistic summarization of long-term trends for understanding change in human behavior. In: Proceedings of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2011, pp. 2080–2087 (2011)Google Scholar
  19. 19.
    Szczepaniak, P., Ochelska, J.: Linguistic summaries of standardized documents. In: Last, M., Szczepaniak, P.S., Volkovich, Z., Kandel, A. (eds.) Advances in Web Intelligence and Data Mining. SCI, vol. 23, pp. 221–232. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    doi:10.4121/c2c3b154-ab26-4b31-a0e8-8f2350ddac11Google Scholar
  21. 21.
    Wilbik, A., Keller, J.M., Bezdek, J.C.: Linguistic prototypes for data from eldercare residents. IEEE Transactions on Fuzzy Systems (2013) (in press)Google Scholar
  22. 22.
    Wilbik, A., Keller, J.M.: A distance metric for a space of linguistic summaries. Fuzzy Sets and Systems 208, 79–94 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Wilbik, A., Keller, J.M., Alexander, G.L.: Linguistic summarization of sensor data for eldercare. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2011), pp. 2595–2599 (2011)Google Scholar
  24. 24.
    Yager, R.R.: A new approach to the summarization of data. Information Sciences 28, 69–86 (1982)CrossRefzbMATHMathSciNetGoogle Scholar
  25. 25.
    Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 9(2), 111–127 (1983)MathSciNetGoogle Scholar
  26. 26.
    Zadeh, L.A.: A prototype-centered approach to adding deduction capabilities to search engines – the concept of a protoform. In: Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2002), pp. 523–525 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anna Wilbik
    • 1
  • Uzay Kaymak
    • 1
  1. 1.Information Systems School of Industrial EngineeringEindhoven University of TechnologyEindhovenThe Netherlands

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