Skip to main content
SpringerLink
Log in

From logic descriptors to granular logic descriptors: a study in allocation of information granularity

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Local sources of knowledge structured in the form of logic descriptors—constructs of fuzzy logic, are arranged together (structured) in the form of a global model coming as a high-level granular logic descriptor. The inherent granularity of the global descriptor of this nature arises as a manifestation of the diversity of the locally available descriptors. The granular descriptor can be expressed with the aid of any of the formal models of information granules including sets, fuzzy sets, rough sets, probabilistic granules and others. The architectural essence of the granular descriptor, which supports a quantification of the variability among the sources of knowledge, is realized through an optimal allocation of information granularity. Information granularity is treated as an important design asset and its allocation throughout the parameters of the logic descriptors helps quantify the diversity of individual sources of knowledge. Various protocols of allocation of information granularity along with an overall quantification of their effectiveness are discussed along with their numeric characterization.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Bargiela A, Pedrycz W (2003) Granular computing: an introduction. Kluwer Academic Publishers, Dordrecht

    Book  Google Scholar 

  • Bargiela A, Pedrycz W (2005) Granular mappings. IEEE Trans Syst Man Cybernet Part A 35:292–297

    Article  Google Scholar 

  • Bargiela A, Pedrycz W (2008) Toward a theory of granular computing for human-centered information processing. IEEE Trans Fuzzy Syst 16:320–330

    Article  Google Scholar 

  • Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    Book  MATH  Google Scholar 

  • Gobi AF, Pedrycz W (2007) Fuzzy modeling through logic optimization. Int J Approx Reason 45:488–510

    Article  MATH  Google Scholar 

  • Hirota K (1981) Concepts of probabilistic sets. Fuzzy Sets Syst 5:31–46

    Article  MathSciNet  MATH  Google Scholar 

  • Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data, system theory. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Pedrycz W (1998) Shadowed sets: representing and processing fuzzy sets. IEEE Trans Syst Man Cybernet Part B 28:103–109

    Article  Google Scholar 

  • Pedrycz W (2005) Knowledge-based clustering: from data to information granules. Wiley, Hoboken

    Book  Google Scholar 

  • Pedrycz W, Gomide F (2007) Fuzzy systems engineering: toward human-centric computing. Wiley, Hoboken

    Book  Google Scholar 

  • Pedrycz W, Bargiela A (2010) Fuzzy clustering with semantically distinct families of variables: Descriptive and predictive aspects. Pattern Recogn Lett 31:1952–1958

    Article  Google Scholar 

  • Pedrycz W, Bargiela A (2012) An optimization of allocation of information granularity in the interpretation of data structures: toward granular fuzzy clustering. IEEE Trans Syst Man Cybernet Part B (To appear)

  • Pedrycz W, Hirota K (2008) A consensus-driven clustering. Pattern Recogn Lett 29:1333–1343

    Article  Google Scholar 

  • Pedrycz W, Rai P (2008) Collaborative clustering with the use of Fuzzy C-Means and its quantification. Fuzzy Sets Syst 159:2399–2427

    Article  MathSciNet  MATH  Google Scholar 

  • Zadeh LA (1997) Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90:111–117

    Article  MathSciNet  MATH  Google Scholar 

  • Zadeh LA (1999) From computing with numbers to computing with words-from manipulation of measurements to manipulation of perceptions. IEEE Trans Circ Syst 45:105–119

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6R 2V4, AB, Canada

    Witold Pedrycz

  2. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Witold Pedrycz

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Witold Pedrycz

Authors
  1. Witold Pedrycz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Witold Pedrycz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pedrycz, W. From logic descriptors to granular logic descriptors: a study in allocation of information granularity. J Ambient Intell Human Comput 4, 411–419 (2013). https://doi.org/10.1007/s12652-012-0127-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-012-0127-x

Keywords

Search

Navigation