Abstract
As one of the important problems of knowledge discovery and data analysis, knowledge reduction can make the discovery of implicit knowledge in data easier and the representation simpler. In this paper, a new approach to knowledge reduction in concept lattices is developed based on irreducible elements, and characteristics of attributes and objects are also analyzed. Furthermore, algorithms for finding attribute and object reducts are provided respectively. The algorithm analysis shows that the approach to knowledge reduction involves less computation and is more tractable compared with the current methods.
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Wang, X., Zhang, W. (2009). Knowledge Reduction in Concept Lattices Based on Irreducible Elements. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Chan, K.C.C. (eds) Transactions on Computational Science V. Lecture Notes in Computer Science, vol 5540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02097-1_7
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DOI: https://doi.org/10.1007/978-3-642-02097-1_7
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