article

Knowledge reduction in real decision formal contexts

Authors:

Changlin Mei, and

Yuejin LvAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 189

Pages 191 - 207

https://doi.org/10.1016/j.ins.2011.11.041

Published: 01 April 2012 Publication History

Abstract

Rule acquisition is one of the main purposes in the analysis of real decision formal contexts. In general, the decision rules derived directly from a real decision formal context are not concise or compact. In order to derive more compact decision rules, this study proposes a rule acquisition oriented framework of knowledge reduction for real decision formal contexts and formulates a corresponding reduction method by constructing a discernibility matrix and its associated Boolean function. The proposed reduction method is applicable to any real decision formal contexts and with the reduced real decision formal contexts, we can obtain more compact decision rules that can imply all the decision rules derived from the initial real decision formal context. Some numerical experiments are conducted to assess the efficiency of the proposed method.

References

[1]

Be¿lohlávek, R., Fuzzy Galois connections. Mathematical Logic Quarterly. v45 i4. 497-504.

[2]

Be¿lohlávek, R., Logic precision in concept lattices. Journal of Logic and Computation. v12. 137-148.

[3]

Be¿lohlávek, R., Concept lattices and order in fuzzy logic. Annals of Pure and Applied Logic. v128 i1-3. 277-298.

[4]

Burusco, A. and Fuentes-González, R., The study of the L-fuzzy concept lattice. Mathware and Soft Computing. v3. 209-218.

[5]

Burusco, A. and Fuentes-González, R., Concept lattices defined from implication operators. Fuzzy Sets and Systems. v114 i3. 431-436.

[6]

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.

[7]

Carpineto, C. and Romano, G., A lattice conceptual clustering system and its application to browsing retrieval. Machine Learning. v10. 95-122.

[8]

Carpineto, C. and Romano, G., Exploiting the potential of concept lattices for information retrieval with CREDO. Journal of Universal Computer Science. v10 i8. 985-1013.

[9]

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.

[10]

I. Düntsch, G. Gediga, Modal-style operators in qualitative data analysis, in: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), 2002, pp. 155-162.

[11]

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.

[12]

Ganter, B. and Wille, R., Formal Concept Analysis. In: Mathematical Foundations, Springer, Berlin.

[13]

Georgescu, G. and Popescu, A., Non-commutative fuzzy Galois connections. Soft Computing. v7. 458-467.

[14]

Jaoua, A., Elloumi, S. and connection, Galois, formal concepts and Galois lattice in real relations: application in a real classifier. The Journal of Systems and Software. v60. 149-163.

[15]

Li, L.F. and Zhang, J.K., Attribute reduction in fuzzy concept lattices based on T implication. Knowledge-Based Systems. v23 i6. 497-503.

[16]

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.

[17]

Popescu, A., A general approach to fuzzy concepts. Mathematical Logic Quarterly. v50 i3. 265-280.

[18]

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.

[19]

Skowron, A., Boolean reasoning for implication rules generation. In: Methodologies for Intelligent Systems, Springer-Verlag. pp. 295-305.

[20]

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.

[21]

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.

[22]

Wei, L., Qi, J.J. and Zhang, W.X., Attribute reduction theory of concept lattice based on decision formal contexts. Science in China: Series F - Information Sciences. v51 i7. 910-923.

[23]

Wille, R., Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (Ed.), Ordered Sets, Reidel, Dordrecht, Boston. pp. 445-470.

[24]

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.

[25]

Yang, H.Z., Leung, Y. and Shao, M.W., Rule acquisition and attribute reduction in real decision formal contexts. Soft Computing. v15 i6. 1115-1128.

[26]

Y.Y. Yao, Concept lattices in rough set theory, in: Proceedings of the 23rd International Meeting of the North American Fuzzy Information Processing Society, 2004.

[27]

Y.Y. Yao, A comparative study of formal concept analysis and rough set theory in data analysis, in: Proceedings of the 4th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2004), Uppsala, Sweden, 2004, pp. 59-68.

[28]

Zhang, W.X., Wei, L. and Qi, J.J., Attribute reduction theory and approach to concept lattice. Science in China: Series F - Information Sciences. v48 i6. 713-726.

[29]

Zhang, W.X., Ma, J.M. and Fan, S.Q., Variable threshold concept lattices. Information Sciences. v177 i22. 4883-4892.

Cited By

Lu YSong JWang PXu T(2022)Label-specific guidance for efficiently searching reductJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21311243:1(1315-1329)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-213112
Yang HQin KHu QYang L(2022)Neighborhood based concept latticeApplied Intelligence10.1007/s10489-022-03828-253:5(6025-6040)Online publication date: 5-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-03828-2
Singh P(2022)Bipolar fuzzy concepts reduction using granular-based weighted entropySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07336-w26:19(9859-9871)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s00500-022-07336-w
Show More Cited By

Index Terms

Knowledge reduction in real decision formal contexts
1. Theory of computation
  1. Logic

Index terms have been assigned to the content through auto-classification.

Recommendations

Knowledge reduction in decision formal contexts

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 ...
Read More
Rule-preserved object compression in formal decision contexts using concept lattices

Rule acquisition is one of the main purposes in the analysis of formal decision contexts. In general, given a formal decision context, some of its objects may not be essential to the rule acquisition. This study investigates the issue of reducing the ...
Read More
Analysis of interval-valued decision formal contexts
Abstract
This study investigates a novel approach to knowledge reduction in interval-valued decision formal contexts. Applying rule acquisition, we formulate a new framework of knowledge reduction for interval-valued decision formal contexts and the ...
Read More

Comments

Information & Contributors

Information

Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 189, Issue

April, 2012

310 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2011.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 April 2012

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

38
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Other Metrics

View Author Metrics

Citations

Cited By

Lu YSong JWang PXu T(2022)Label-specific guidance for efficiently searching reductJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21311243:1(1315-1329)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-213112
Yang HQin KHu QYang L(2022)Neighborhood based concept latticeApplied Intelligence10.1007/s10489-022-03828-253:5(6025-6040)Online publication date: 5-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-03828-2
Singh P(2022)Bipolar fuzzy concepts reduction using granular-based weighted entropySoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07336-w26:19(9859-9871)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s00500-022-07336-w
Lin YLi JLiao SZhang JLiu J(2020)Reduction of fuzzy-crisp concept lattice based on order-class matrixJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20148539:5(8001-8013)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/JIFS-201485
Dias SZárate LSong MVieira NKumar C(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
https://dl.acm.org/doi/10.3233/IDA-194569
Lin YLi JWang H(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
https://dl.acm.org/doi/10.1007/s00500-020-04941-5
Tsang EFan BChen DXu WLi W(2020)Multi-level cognitive concept learning method oriented to data sets with fuzziness: a perspective from featuresSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-04144-724:5(3753-3770)Online publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1007/s00500-019-04144-7
Singh P(2019)Complex Fuzzy Concept LatticeNeural Processing Letters10.1007/s11063-018-9884-749:3(1511-1526)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11063-018-9884-7
Formica A(2019)Similarity reasoning in formal concept analysisKnowledge and Information Systems10.1007/s10115-018-1252-460:2(715-739)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10115-018-1252-4
Shi LYang H(2018)Object granular reduction of fuzzy formal contextsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-1790934:1(633-644)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.3233/JIFS-17909
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents