Building Granular Systems - from Concepts to Applications

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

  • This work was partially supported by the Polish National Science Centre (NCN) grant DEC-2012/05/B/ST6/03215 as well as by the Polish National Centre for Research and Development (NCBiR) grants O ROB/0010/03/001 and PBS2/B9/20/2013.

Abstract

Granular Computing (GrC) is a domain of science aiming at modeling computations and reasoning that deals with imprecision, vagueness and incompleteness of information. Computations in GrC are performed on granules which are obtained as a result of information granulation. Principal issues in GrC concern processes of representation, construction, transformation and evaluation of granules. It also requires aligning with some of the fundamental computational issues concerning, e.g., interaction and adaptation. The paper outlines the current status of GrC and provides the general overview of the process of building granular solutions to challenges posed by various real-life problems involving granularity. It discusses the steps that lead from raw data and imprecise/vague specification towards a complete, useful application of granular paradigm.

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Change history

  • 19 December 2018

    The acknowledgement section of this paper originally referred to grant DEC-2013/09/B/ST6/01568. The reference to this grant has been removed from the acknowledgement section at the request of one of the authors.

Notes

  1. 1.

    http://dblp.uni-trier.de/db/conf/grc/index.html.

  2. 2.

    http://dblp.uni-trier.de/db/conf/rsfdgrc/index.html.

  3. 3.

    http://www.inderscience.com/jhome.php?jcode=ijgcrsis.

  4. 4.

    http://www.springer.com/engineering/computational+intelligence+and+complexity/journal/41066.

References

  1. Lin, T.Y., et al.: Granular computing - topical section. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 4283–4435. Springer, New York (2009)

    Google Scholar 

  2. Yao, Y., Zhong, N.: Granular computing. In: Wah, B., Wah, B.M. (eds.) Wiley Encyclopedia of Computer Science and Engineering. Wiley, New York (2008)

    Google Scholar 

  3. Pedrycz, W.: History and development of granular computing. In: UNESCO-EOLSS Joint Committee, (ed.) Encyclopedia of Life Support Systems (EOLSS). Eolss Publishers, Paris (2012)

    Google Scholar 

  4. Apolloni, B., Pedrycz, W., Bassis, S., Malchiodi, D.: The Puzzle of Granular Computing. Studies in Computational Intelligence, vol. 138. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  5. Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordrecht (2003)

    MATH  Google Scholar 

  6. Bello, R., Falcón, R., Pedrycz, W.: Granular Computing: At the Junction of Rough Sets and Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol. 234. Springer, Heidelberg (2010)

    Google Scholar 

  7. Pedrycz, W.: Granular Computing Analysis and Design of Intelligent Systems. CRC Press, Taylor and Francis, Boca Raton (2013)

    Google Scholar 

  8. Polkowski, L., Artiemjew, P.: Granular Computing in Decision Approximation: An Application of Rough Mereology. Intelligent Systems Reference Library. Springer, Switzerland (2015)

    MATH  Google Scholar 

  9. Stepaniuk, J.: Rough-Granular Computing in Knowledge Discovery and Data Mining. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  10. Inuiguchi, M., Hirano, S., Tsumoto, S. (eds.): Rough Set Theory and Granular Computing. Studies in Fuzziness and Soft Computing, vol. 125. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  11. Lin, T.Y., Yao, Y., Zadeh, L.A. (eds.): Rough Sets, Granular Computing and Data Mining. Studies in Fuzziness and Soft Computing. Physica-Verlag, Heidelberg (2001)

    Google Scholar 

  12. Pal, S.K., Skowron, A. (eds.): Rough Fuzzy Hybridization: A New Trend in Decision-Making. Springer, Singapore (1999)

    MATH  Google Scholar 

  13. Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  14. Pedrycz, W. (ed.): Granular Computing: An Emerging Paradigm. Studies in Fuzziness and Soft Computing, vol. 70. Physica-Verlag, Heidelberg (2001)

    MATH  Google Scholar 

  15. Pedrycz, W. (ed.): Knowledge-Based Clustering. From Data to Information Granules. Wiley, New York (2005)

    MATH  Google Scholar 

  16. Bargiela, A., Pedrycz, W. (eds.): Human-Centric Information Processing Through Granular Modelling. Studies in Computational Intelligence, vol. 182. Springer, Heidelberg (2009)

    Google Scholar 

  17. Pedrycz, W., Chen, S.M. (eds.): Granular Computing and Intelligent Systems Design with Information Granules of Higher Order and Higher Type. Studies in Computational Intelligence, vol. 502. Springer, Heidelberg (2011)

    Google Scholar 

  18. Pedrycz, W., Chen, S.M. (eds.): Information Granularity, Big Data, and Computational Intelligence. Studies in Big Data, vol. 8. Springer, Heidelberg (2015)

    Google Scholar 

  19. Yao, J.T. (ed.): Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation. IGI Global, Hershey (2010)

    Google Scholar 

  20. Zadeh, L.A., Kacprzyk, J. (eds.): Computing with Words in Information/Intelligent Systems. Physica-Verlag, Heidelberg (1999)

    MATH  Google Scholar 

  21. Zhang, L., Zhang, B. (eds.): Quotient Space Based Problem Solving: A Theoretical Foundation of Granular Computing. Elsevier, Amsterdam (2014)

    MATH  Google Scholar 

  22. Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–127 (1997)

    MathSciNet  MATH  Google Scholar 

  23. Zadeh, L.A.: Generalized theory of uncertainty (GTU) - principal concepts and ideas. Comput. Stat. Data Anal. 51, 15–46 (2006)

    MathSciNet  MATH  Google Scholar 

  24. Zadeh, L.A. (ed.): Computing with Words: Principal Concepts and Ideas. Studies in Fuzziness and Soft Computing, vol. 277. Springer, Heidelberg (2012)

    MATH  Google Scholar 

  25. Keefe, R.: Theories of Vagueness. Cambridge Studies in Philosophy. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  26. Baker, G., Hacker, P.: Wittgenstein: Understanding and Meaning. Analytical Commentary on the Philosophical Investigations, Part II: Exegesis 1–184, vol. 1, 2nd edn. Wiley-Blackwell Publishing, Oxford (2004)

    Google Scholar 

  27. Zadeh, L.A.: Fuzzy logic = Computing with words. IEEE Trans. Fuzzy Syst. 2, 103–111 (1996)

    Google Scholar 

  28. Zadeh, L.A.: From computing with numbers to computing with words - from manipulation of measurements to manipulation of perceptions. IEEE Trans. Circuits Syst. 45, 105–119 (1999)

    MathSciNet  MATH  Google Scholar 

  29. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. SMC–3, 28–44 (1973)

    MathSciNet  MATH  Google Scholar 

  30. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  31. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)

    MathSciNet  MATH  Google Scholar 

  32. Pawlak, Z., Skowron, A.: Rough sets: some extensions. Inf. Sci. 177(1), 28–40 (2007)

    MathSciNet  MATH  Google Scholar 

  33. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    MATH  Google Scholar 

  34. Moore, R., Kearfott, R.B., Cloud, M.J.: Introduction to Interval Analysis. SIAM, Philadelphia (2009)

    MATH  Google Scholar 

  35. Pedrycz, W.: From fuzzy sets to shadowed sets: interpretation and computing. Int. J. Intell. Syst. 24(1), 48–61 (2009)

    MATH  Google Scholar 

  36. Sakai, H., Okuma, H., Nakata, M., Ślȩzak, D.: Stable rule extraction and decision making in rough non-deterministic information analysis. Int. J. Hybrid Intell. Syst. 8(1), 41–57 (2011)

    MATH  Google Scholar 

  37. Sakai, H., Wu, M., Nakata, M.: Apriori-based rule generation in incomplete information databases and non-deterministic information systems. Fundamenta Informaticae 130(3), 343–376 (2014)

    MathSciNet  MATH  Google Scholar 

  38. Ślȩzak, D., Synak, P., Wojna, A., Wróblewski, J.: Two database related interpretations of rough approximations: data organization and query execution. Fundamenta Informaticae 127(1–4), 445–459 (2013)

    Google Scholar 

  39. Pankratieva, V.V., Kuznetsov, S.O.: Relations between proto-fuzzy concepts, crisply generated fuzzy concepts, and interval pattern structures. Fundamenta Informaticae 115(4), 265–277 (2012)

    MathSciNet  MATH  Google Scholar 

  40. Lin, T.Y.: Data mining and machine oriented modeling: a granular computing approach. Appl. Intell. 13(2), 113–124 (2000)

    Google Scholar 

  41. Polkowski, L., Skowron, A.: Rough mereological calculi of granules: a rough set approach to computation. Comput. Intell. 17(3), 472–492 (2001)

    MathSciNet  Google Scholar 

  42. Skowron, A., Stepaniuk, J., Peters, J.F., Świniarski, R.W.: Calculi of approximation spaces. Fundamenta Informaticae 72, 363–378 (2006)

    MathSciNet  MATH  Google Scholar 

  43. Krasuski, A., Jankowski, A., Skowron, A., Ślȩzak, D.: From sensory data to decision making: a perspective on supporting a fire commander. In: 2013 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Atlanta, Georgia, USA, 17–20 November 2013, Workshop Proceedings, pp. 229–236. IEEE Computer Society (2013)

    Google Scholar 

  44. Szczuka, M.S., Skowron, A., Stepaniuk, J.: Function approximation and quality measures in rough-granular systems. Fundamenta Informaticae 109(3), 339–354 (2011)

    MathSciNet  MATH  Google Scholar 

  45. Pedrycz, W.: The principle of justifiable granularity and an optimization of information granularity allocation as fundamentals of granular computing. J. Inf. Process. Syst. 7(3), 397–412 (2011)

    Google Scholar 

  46. Apolloni, B., Pedrycz, W., Bassis, S., Malchiodi, D.: The Puzzle of Granular Computing. Studies in Computational Intelligence, vol. 138. Springer, Heidelberg (2008)

    MATH  Google Scholar 

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Correspondence to Marcin Szczuka .

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Szczuka, M., Jankowski, A., Skowron, A., Ślęzak, D. (2015). Building Granular Systems - from Concepts to Applications. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_22

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