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Formal Concept Analysis in Relational Database and Rough Relational Database

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Języki publikacji
EN
Abstrakty
EN
Since its foundation in the early 1980's, Formal Concept Analysis (FCA) has been used in many applications in data analysis, information retrieval, and knowledge discovery. In this paper, we suggest to exploit the framework of relational database model (RDM) and rough relational database model (RRDM) for Formal Concept Analysis. The basic idea is as follows. We firstly treat any relation (R,A) of RDM as a many-valued context of FCA. But for the rough relations of RRDM, we define a special kind of many-valued context - rough-relational context in FCA (In this kind of context, every attribute value is a subset, but not an element, of the corresponding attribute domain), and treat any rough relation (R,A) of RRDM as a rough-relational context of FCA. Correspondingly, the definitions for concepts or rough concepts in context or rough-relational context (R,A) are given. The basic properties about these concepts or rough concepts in (R,A) are also discussed.
Wydawca
Rocznik
Strony
435--451
Opis fizyczny
bibliogr. 30 poz., tab.
Twórcy
autor
autor
autor
  • Room #538, Institute of Computing Technology, Chinese Academy of Sciences, 6#, South Road, Kexueyuan, Haidian District, Beijing, 100080, P.R. China, jiangkong@163.net
Bibliografia
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  • [7] Ganter, B., Wille, R.: Conceptual scaling. In: Roberts, F., Eds., Applications of combinatorics and graph theory to the biological and social sciences, Springer-Verlag, New York (1989) 139-167.
  • [8] Ganter, B., Wille, R.: Formal Concept Analysis: mathematical foundations. Springer-Verlag, Berlin (1999).
  • [9] Hereth, J.: Relational Scaling and Databases. In: Proc. of the 10th International Conference on Conceptual Structures: Integration and Interfaces, LNCS 2393, Springer Verlag (2002) 62-76.
  • [10] Hsieh, N. C., Chiang, D. A., Wang, T. S.: Answers to Database Queries Concerning Imprecise Information in Logical Fuzzy Relational Databases. Tamkang Journal of Science and Engineering, 7(3) (2004) 149-160.
  • [11] Jiang, F., Sui, Y. F. and Cao, C. G.: Rough Contexts and Rough-Valued Contexts. In: Proc. of the first Int. Conf. on Rough Set and Knowledge Technology (RSKT 2006), LNAI 4062, Chongqing, P.R. China (2006) 176-183.
  • [12] Kent, R. E.: Rough Concept Analysis. In: Ziarko, W. (Ed.), Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer-Verlag (1994) 248-255.
  • [13] Pagliani, P.: Modalizing Relations by means of Relations: a general framework for two basic approaches to Knowledge Discovery in Database. In: M. Gevers (Ed.), Proc. of the 7th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU '98), Paris, France (1998) 1175-1182.
  • [14] Pagliani, P.: Transforming Information Systems. In: Proc. of the Tenth International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, University of Regina, Canada (2005) 660-670.
  • [15] Pasquier, N., Bastide, Y., Taouil, T., Lakhal, L.: Efficient Mining of Association Rules Using Closed Itemset Lattices. Information Systems, 24(1) (1999) 25-46.
  • [16] Pawlak, Z.: Rough sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991).
  • [17] Pawlak, Z., Grzymala-Busse, J. W., Slowinski, R., Ziarko, W.: Rough sets. Communications of the ACM, 38(11) (1995) 89-95.
  • [18] Priss, U.: Relational Concept Analysis: Semantic Structures in Dictionaries and Lexical Databases. (PhD Thesis) Verlag Shaker, Aachen (1998).
  • [19] Priss, U.: Establishing connections between Formal Concept Analysis and Relational Databases. In: Proc. of the 13th International Conference on Conecptual Structures, Common Semantics for Sharing Knowledge: Contributions to ICCS 2005), Kassel, Germany (2005) 132-145.
  • [20] Priss, U.: An FCA interpretation of Relation Algebra. In: Proc. of the 4th International Conference on Formal Concept Analysis (ICFCA 2006), Dresden, Germany (2006) 248-263.
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  • [22] Sui, Y. F.,Wang, J., Jiang, Y. C.: Rough Relational Database: the Basic Definitions. Computer Science, 28(5) (2001) 122-124.
  • [23] Sui, Y. F., Xia, Y. M.,Wang, J.: The Information Entropy of Rough Relational Databases. In: Proc. of the 9th Intl. Conf. on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003), Chongqing, China (2003) 320-324.
  • [24] Tonella, P.: Concept analysis for module restructuring. IEEE Transactions on Software Engineering, 27(4) (2001) 351-363.
  • [25] Valtchev, P., Hacene, M. R., Huchard, M., Roume, C.: Extracting Formal Concepts out of Relational Data. In: Proc. of the 4th Intl. Conf. Journes de l'Informatique Messine (JIM'03),Metz, France (2003) 37-49.
  • [26] Wille, R.: Restructuring Lattice theory: An Approach Based on Hierarchies of Concepts. Ordered Sets, Reidtel, D., Dordrecht (1982) 445-470.
  • [27] Wille, R.: Conceptual structures of multi-contexts. In: Eklund, P., et al., Eds., Conceptual Structures: Knowledge Representation as Interlingua, number 1114 in LNAI, Springer-Verlag, Berlin (1996) 23-39.
  • [28] Yao, Y. Y.: A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis. In: Tsumoto, S., et al. Eds., Proc. of the 4th International Conference on Rough Sets and Current Trends in Computing (RSCTC'2004), LNAI 3066, Springer-Verlag, Uppsala, Sweden (2004) 59-68.
  • [29] Yao, Y. Y.: Concept Lattices in Rough Set Theory. In: Dick, S., et al. Eds., Proc. of 2004 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2004), Banff, Alberta, Canada (2004) 796-801.
  • [30] Yao, Y. Y., Chen, Y. H.: Rough Set Approximations in Formal Concept Analysis. In: Dick, S., et al. Eds., Proc. of 2004 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2004), Banff, Alberta, Canada (2004) 73-78.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS5-0014-0021
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