Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
article

Knowledge reduction in decision formal contexts

Published: 01 July 2011 Publication History
  • Get Citation Alerts
  • Abstract

    This study deals with the problem of knowledge reduction in decision formal contexts. From the perspective of rule acquisition, a new framework of knowledge reduction for decision formal contexts is formulated and a corresponding reduction method is also developed by using the discernibility matrix and Boolean function. The presented framework of knowledge reduction is for general decision formal contexts, and based on the proposed reduction method, knowledge hidden in a decision formal context can compactly be unravelled in the form of implication rules.

    References

    [1]
    Wille, R., Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (Ed.), Ordered Sets, Reidel, Dordrecht-Boston. pp. 445-470.
    [2]
    Belohlavek, R., De Baets, B., Outrata, J. and Vychodil, V., Inducing decision trees via concept lattices. International Journal of General Systems. v38 i4. 455-467.
    [3]
    Ganter, B. and Wille, R., . 1999. Mathematical Foundations, 1999.Springer, Berlin.
    [4]
    Ho, T.B., An approach to concept formation based on formal concept analysis. IEICE Transactions on Information and Systems. vE782D i5. 553-559.
    [5]
    Nguyen, P.H.P. and Corbett, D., A basic mathematical framework for conceptual graphs. IEEE Transactions on Knowledge and Data Engineering. v18 i2. 261-271.
    [6]
    Wang, X. and Zhang, W.X., Relations of attribute reduction between object and property oriented concept lattices. Knowledge-Based Systems. v21 i5. 398-403.
    [7]
    Stumme, G., Wille, R. and Wille, U., Conceptual knowledge discovery in databases using formal concept analysis methods. Lecture Notes in Artificial Intelligence. v1510. 450-458.
    [8]
    P. Valtchev, R. Missaoui, R. Godin, Formal concept analysis for knowledge discovery and data mining: the new challenge, in: Proceedings of the 2nd International Conference on Formal Concept Analysis, 2004, pp. 352-371.
    [9]
    Zhang, W.X., Ma, J.M. and Fan, S.Q., Variable threshold concept lattices. Information Sciences. v177 i22. 4883-4892.
    [10]
    Godin, R., Incremental concept formation algorithm based on Galois (concept) lattice. Computation Intelligence. v11. 246-267.
    [11]
    Carpineto, C. and Romano, G., Exploiting the potential of concept lattices for information retrieval with CREDO. Journal of Universal Computer Sciences. v10 i8. 985-1013.
    [12]
    Cole, R., Eklund, P. and Stumme, G., Document retrieval for e-mail search and discovery using formal concept analysis. Applied Artificial Intelligence. v17 i3. 257-280.
    [13]
    Carpineto, C. and Romano, G., Galois: an order-theoretic approach to conceptual clustering. In: Utgoff, P. (Ed.), Proceedings of the 1993 International Conference on Machine Learning (ICML'93), Elsevier, Amherst. pp. 33-40.
    [14]
    Carpineto, C. and Romano, G., A lattice conceptual clustering system and its application to browsing retrieval. Machine Learning. v10. 95-122.
    [15]
    Wille, R., Formal concept analysis as mathematical theory of concepts and concepts hierarchies. In: Ganter, B. (Ed.), Formal Concept Analysis, Springer-Verlag. pp. 1-33.
    [16]
    Yang, S.Q., Ding, S.L., Cai, S.Z. and Ding, Q.L., An algorithm of constructing concept lattices for CAT with cognitive diagnosis. Knowledge-Based Systems. v21 i8. 852-855.
    [17]
    Wu, Q. and Liu, Z.T., Real formal concept analysis based on grey-rough set theory. Knowledge-Based Systems. v22 i1. 38-45.
    [18]
    Pawlak, Z., Rough sets. International Journal of Computer and Information Sciences. v11. 341-356.
    [19]
    Leung, Y., Fischer, M.M., Wu, W.Z. and Mi, J.S., A rough set approach for the discovery of classification rules in interval-valued information systems. International Journal of Approximate Reasoning. v47 i2. 233-246.
    [20]
    Meng, Z.Q. and Shi, Z.Z., A fast approach to attribute reduction in incomplete decision systems with tolerance relation-based rough sets. Information Sciences. v179 i16. 2774-2793.
    [21]
    Mi, J.S., Wu, W.Z. and Zhang, W.X., Approaches to knowledge reductions based on variable precision rough sets model. Information Sciences. v159 i3-4. 255-272.
    [22]
    Wu, W.Z., Attribute reduction based on evidence theory in incomplete decision systems. Information Sciences. v178 i5. 1355-1371.
    [23]
    Kent, R.E., Rough concept analysis: a synthesis of rough sets and formal concept analysis. Fundamenta Informaticae. v27. 169-181.
    [24]
    Y.Y. Yao, A comparative study of formal concept analysis and rough set theory in data analysis, in: Proceedings of 4th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2004), Uppsala, Sweden, 2004, pp. 59-68.
    [25]
    Wei, L. and Qi, J.J., Relation between concept lattice reduction and rough set reduction. Knowledge-Based Systems. v23 i8. 934-938.
    [26]
    Elloumi, S., Jaam, J., Hasnah, A., Jaoua, A. and Nafkha, I., A multi-level conceptual data reduction approach based on the Lukasiewicz implication. Information Sciences. v163 i4. 253-262.
    [27]
    Li, L.F. and Zhang, J.K., Attribute reduction in fuzzy concept lattices based on T implication. Knowledge-Based Systems. v23 i6. 497-503.
    [28]
    Zhang, W.X., Wei, L. and Qi, J.J., Attribute reduction theory and approach to concept lattice. Science in China F. v48 i6. 713-726.
    [29]
    Liu, M., Shao, M.W., Zhang, W.X. and Wu, C., Reduction method for concept lattices based on rough set theory and its application. Computers and Mathematics with Applications. v53 i9. 1390-1410.
    [30]
    Aswani Kumar, Ch. and Srinivas, S., Concept lattice reduction using fuzzy K-means clustering. Expert Systems with Applications. v37 i3. 2696-2704.
    [31]
    Mi, J.S., Leung, Y. and Wu, W.Z., Approaches to attribute reduction in concept lattices induced by axialities. Knowledge-Based Systems. v23 i6. 504-511.
    [32]
    Wang, H. and Zhang, W.X., Approaches to knowledge reduction in generalized consistent decision formal context. Mathematical and Computer Modelling. v48 i11-12. 1677-1684.
    [33]
    Wei, L., Qi, J.J. and Zhang, W.X., Attribute reduction theory of concept lattice based on decision formal contexts. Science in China F. v51 i7. 910-923.
    [34]
    Wu, W.Z., Leung, Y. and Mi, J.S., Granular computing and knowledge reduction in formal contexts. IEEE Transactions on Knowledge and Data Engineering. v21 i10. 1461-1474.
    [35]
    Li, J., Mei, C. and Lv, Y., A heuristic knowledge-reduction method for decision formal contexts. Computers and Mathematics with Applications. v61 i4. 1096-1106.
    [36]
    Skowron, A., Boolean reasoning for implication rules generation. In: Methodologies for Intelligent Systems, Springer-Verlag. pp. 295-305.
    [37]
    Skowron, A. and Rauszer, C., The discernibility matrices and functions in information systems. In: Intelligent Decision Support: Handbook of Application and Advances of the Rough Sets Theory, Kluwer Academic Publishers. pp. 331-362.

    Cited By

    View all
    • (2024)Attribute Transfer and Knowledge Discovery Based on Formal ContextProceedings of the 2024 International Conference on Computer and Multimedia Technology10.1145/3675249.3675262(75-78)Online publication date: 24-May-2024
    • (2023)Fuzzy Decision Rule-Based Online Classification Algorithm in Fuzzy Formal Decision ContextsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.325095531:9(3263-3277)Online publication date: 31-Aug-2023
    • (2022)Rule reductions of decision formal context based on mixed informationApplied Intelligence10.1007/s10489-022-04194-953:12(15459-15475)Online publication date: 18-Nov-2022
    • Show More Cited By

    Index Terms

    1. Knowledge reduction in decision formal contexts
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image Knowledge-Based Systems
      Knowledge-Based Systems  Volume 24, Issue 5
      July, 2011
      169 pages

      Publisher

      Elsevier Science Publishers B. V.

      Netherlands

      Publication History

      Published: 01 July 2011

      Author Tags

      1. Concept lattice
      2. Decision formal context
      3. Formal concept analysis
      4. Knowledge reduction
      5. Rule acquisition

      Qualifiers

      • Article

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 11 Aug 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Attribute Transfer and Knowledge Discovery Based on Formal ContextProceedings of the 2024 International Conference on Computer and Multimedia Technology10.1145/3675249.3675262(75-78)Online publication date: 24-May-2024
      • (2023)Fuzzy Decision Rule-Based Online Classification Algorithm in Fuzzy Formal Decision ContextsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.325095531:9(3263-3277)Online publication date: 31-Aug-2023
      • (2022)Rule reductions of decision formal context based on mixed informationApplied Intelligence10.1007/s10489-022-04194-953:12(15459-15475)Online publication date: 18-Nov-2022
      • (2022)A novel approach to attribute reduction and rule acquisition of formal decision contextApplied Intelligence10.1007/s10489-022-04139-253:11(13834-13851)Online publication date: 17-Oct-2022
      • (2022)Uncover the reasons for performance differences between measurement functions (Provably)Applied Intelligence10.1007/s10489-022-03726-753:5(5179-5198)Online publication date: 20-Jun-2022
      • (2022)Incremental method of generating decision implication canonical basisSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-06452-326:3(1067-1083)Online publication date: 1-Feb-2022
      • (2020)Gaussian kernel fuzzy rough based attribute reductionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-19163339:1(679-695)Online publication date: 1-Jan-2020
      • (2020)Extraction of qualitative behavior rules for industrial processes from reduced concept latticeIntelligent Data Analysis10.3233/IDA-19456924:3(643-663)Online publication date: 1-Jan-2020
      • (2020)Measures of uncertainty for knowledge basesKnowledge and Information Systems10.1007/s10115-019-01363-062:2(611-637)Online publication date: 1-Feb-2020
      • (2020)Granular matrix method of attribute reduction in formal contextsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-04941-524:21(16303-16314)Online publication date: 1-Nov-2020
      • Show More Cited By

      View Options

      View options

      Get Access

      Login options

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media