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The Construction of Object-oriented Multi-granularity Concept Lattice
LI Ke-wen, LV Meng-meng, SHAO Ming-wen
2020, 37 (5):
591-605.
doi: 10.3969/j.issn.1005-3085.2020.05.006
How to reduce the complexity in the construction of concept lattice is an important research topic in formal concept analysis. Granular computing can analyze and solve problems from multiple angles which has been an effective tool for solving problems in the field of artificial intelligence. Aiming at the rapid construction of multi-granularity concept lattice, based on the granular computing theory, this paper investigates the relation between the extent and intent of concept before and after the change of the attribute granularity and defines different types of concepts. Then, Zoom algorithms composed of Zoom-in algorithm and Zoom-out algorithm are proposed, which achieve the transformation among object-oriented concept lattices with different attribute granularity combinations. Based on the original concept lattice, Zoom algorithms implement the construction of the new concept lattice directly, which avoid the trivial steps of the traditional construction method of concept lattice, and improve the efficiency of the construction of concept lattice. Therefore, the optimal granularity combination of the concept lattice can be quickly determined, which ultimately helps data mining and knowledge discovery from the data.
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