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Concept Lattices of Subcontexts of a Context

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Języki publikacji
EN
Abstrakty
EN
As an effective tool for data analysis and knowledge processing, the theory of concept lattices has been studied extensively and applied to various fields. In order to discover useful knowledge, one often ignores some attributes according to a particular purpose and merely considers the subcontexts of a rather complex context. In this paper, we make a deep investigation on the theory of concept lattices of subcontexts. An approach to construct the concept lattice of a context is first presented by means of the concept lattices of its subcontexts. Then the concept lattices induced by all subcontexts of the context are considered as a set, and an order relation is introduced into the set. It is proved that the set together with the order relation is a complete lattice. Finally, the top element and the bottom element of the complete lattice are also obtained.
Wydawca
Rocznik
Strony
157--169
Opis fizyczny
bibliogr. 27 poz.
Twórcy
autor
autor
  • Department of Applied Mathematics and Physics, Institute of Science PLA University of Science and Technology Nanjing 211101 Jiangsu, P. R. China, bblylm@126.com
Bibliografia
  • [1] Carpineto, C., Romano, G.: Galois: an order-theoretic approach to conceptual clustering, Proceedings of the Tenth International Conference on Machine Learning, ICML 293, Morgan Kaufmann Prublishers, Amherst, MA, USA, 1993, 33-40.
  • [2] Davey, B.A., Priestley, H.A.: Introduction to Lattices and Order, Second edition, Cambridge University Press, 2002.
  • [3] Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer-Verlag, New York, 1999.
  • [4] Godin, R.: Incremental concept formation algorithm based on Galois (concept) lattices, Computational Intelligence, 11(2), 1995, 246-267.
  • [5] Gediga, G., Düntsch, I.: Modal-style operators in qualitative data analysis, Proceedings of the 2002 IEEE International Conference on Data Mining, 2002, 155-162.
  • [6] Grigoriev, P. A., Yevtushenko, S. A.: Elements of an agile discovery environment, Proceedings of 6th International Conference on Discovery Science (Grieser, G., et al., Eds.), LNAI 2843, Springer-Verlag, Berlin, 2003, 309-316.
  • [7] Ho, T. B.: An approach to concept formation based on formal concept analysis, IEICE Trans. Information and Systems, E78-D(5), 1995, 553-559.
  • [8] Hu, K., Sui, Y., Lu, Y., Wang, J.,and Shi, C.: Concept approximation in concept lattice, Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (Cheung, D., Williams, G. J., Li, Q., Eds. ), LNCS 2035, 2001, 167-173.
  • [9] Järvinen, J.: A Representation of dependence spaces and some basic algorithms. Fundamenta Informaticae 30 1997, 369-382.
  • [10] Järvinen, J.: Representations of information systmes and dependence spaces, and some basic algorithems, Licentiate-s Thesis, University of Turku, April 1997.
  • [11] Järvinen, J.: Knowledge representation and rough sets, PHD Dissertations, University of Turku, Turku, Finland, 1999.
  • [12] Järvinen, J.: Pawlak-s information systems in terms of Galois connections and functional dependencies, Fundamenta Informatica, 75, 2007, 315-330.
  • [13] Liu, M., Shao, M. W., Zhang, W. X., Wu, C.: Reduction method for concept lattices based on rough set theory and its application, Computers and Mathematics with Applications, 53(9), 2007, 1390-1410.
  • [14] Novotny, M.: Dependence spaces of information systems. Incomplete Information Rough Set Analysis (Ortowska, E., Ed.), Physica-Veolag, 1998, 193-289.
  • [15] Oosthuizen, G. D.: The application of concept lattice to machine learning, Technical Report, University of Pretoria, South Africa, 1996.
  • [16] Saquer, J., Deogun, J.: Formal rough concept analysis, Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, LNCS 1711, Springer-Verlag, Berlin, 1999, 91-99.
  • [17] Saquer, J., Deogun, J.: Concept approximations based on rough sets and similarity measures, International Journal of Applied Mathematics and Computer Science, 11(3), 2001, 655-674.
  • [18] Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts, in: Ordered Sets (Rival, I., Ed.), Reidel, Dordrecht-Boston, 1982, 445-470.
  • [19] Wang, X., Ma, J. M.: A novel approach to attribute reduction in concept lattices, Proceedings of Rough Set and Knowledge Technology (Wang, G., et al. Eds.), LNAI 4062, Springer-Verlag, Berlin, 2006, 522-529.
  • [20] Wang, X., Zhang, W. X.: Relations of attribute reduction between object and property oriented concept lattices, Knowledge-Based Systems, 21(5), 2008, 398-403.
  • [21] Wang, X., Zhang, W. X.: Attribute dependence and reduction in formal contexts, International Journal of Computer Science and Knowledge Engineering, 2(1), 2008, 35-49.
  • [22] Yao, Y.Y.: Rough set approximations in formal concept analysis, Proceedings of 2004 AnnualMeeting of the North American Fuzzy Information Processing Society (Dick S., et al., Eds.), 2004, 73-78.
  • [23] Yao, Y. Y.: A comparative study of formal concept analysis and rough set theory in data analysis, Proceedings of 3rd International Conference on Rough Sets and Current Trends in Computing (Tsumoto, S., Slowinski, R., Komorowski, J., Eds.), LNAI 3066, 2004, 59-68.
  • [24] Yao, Y. Y.: Concept lattices in rough set theory, Proceedings of 23rd International Meeting of the North American Fuzzy Information Processing Society (Dick S., et al., Eds.), 2004, 796-801.
  • [25] Zhang,W. X., Wei, L., Qi, J. J.: Attribute reduction theory and approach to concept lattice, Science in China Series F-Information Science, 48(6), 2005, 713-726.
  • [26] Zhang,W. X.,Wei, L., Qi, J. J.: Attribute reduction in concept lattice based on discernibility matrix, Proceedings of Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing (´Sle¸zak, D., et al., Eds.), LNAI 3642, Springer-Verlag, Berlin, 2005, 157-165.
  • [27] Zhang, W. X., Qiu, G. F: Uncertain Decision Making Based on Rough Sets, Tsinghua University Publishing House, 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0004-0011
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