计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 18-27.doi: 10.11896/jsjkx.230600049
王太滨1, 李德玉1,2, 翟岩慧1,2
WANG Taibin1, LI Deyu1,2, ZHAI Yanhui1,2
摘要： 多粒度形式概念分析是数据挖掘与知识发现的重要工具。文中研究了覆盖多粒度下的形式概念的粗化和细化更新方法。首先,举例说明了现有的概念粗化更新算法可能导致概念缺失,通过分析缺失概念的本质特征,对现有概念粗化算法进行补充,并证明了新算法的正确性。其次,举例说明了现有的概念细化更新算法可能会生成冗余概念,通过分析冗余细概念的内涵特性,对现有的概念细化更新算法进行了优化,并证明了新算法生成结果的无冗余性,具有更低的时间复杂度。最后,通过实验验证了所提算法的有效性。
中图分类号:
| [1]GANTER B,WILLE R.Formal concept analysis-mathematical foundations[M].Berlin:Springer,1999. [2]ZHAI Y H,JIA N,ZHANG S X,et al.Study on deduction process and inference methods of decision implications[J].International Journal of Machine Learning and Cybernetics,2022,13(7):1959-1979. [3]ZHAI Y H,QI J J,LI D Y,et al.The structure theorem of three-way concept lattice[J].International Journal of Approximate Reasoning,2022,146:157-173. [4]SAMPATH S,SPRENKLE S,GIBSON E,et al.Applying concept analysis to user-session-based testing of web applications[J].IEEE Transactions on Software Engineering,2007,33(10):643-658. [5]ZHANG S X,LI D Y,ZHAI Y H,et al.A comparative study of decision implication,concept rule and granular rule[J].Information Sciences,2020,508:33-49. [6]HAO C,FAN M,LI J H,et al.Optimal Scale Selection in Multi-scale Contexts Based on Granular Scale Rules[J].Pattern Re-cognition and Artificial Intelligence,2016,29(3):272-280. [7]NIU J J,CHEN D G,LI J H,et al.A dynamic rule-based classification model via granular computing[J].Information Sciences,2022,584:325-341. [8]LI J H,WU W Z,DENG S.Multi-scale theory in formal concept analysis[J].Journal of Shandong University(Nature Science),2019,54(2):30-40. [9]LI J H,WANG F,WU W Z,et al.Review of Multi-granularity Data Analysis Methods Based on Granular Computing[J].Journal of Data Acquisition and Processing,2021,36(3):418-435. [10]BELOHLAVEK R,DE BAETS B,KONECNY J.Granularity of attributes in formal concept analysis[J].Information Sciences,2014,260:149-170. [11]SHE Y H,HE X L,QIAN T,et al.A theoretical study on object-oriented and property-oriented multi-scale formal concept analysis[J].International Journal of Machine Learning and Cybernetics,2019,10:3263-3271. [12]SHAO M W,LV M M,LI K W,et al.The construction of attribute(object)-oriented multi-granularity concept lattices[J].International Journal of Machine Learning and Cybernetics,2020,11:1017-1032. [13]LI J H,HE J J.Uncertainty measurement and optimal granularity selection for multi-granularity formal context[J].Journal of Control and Decision,2022,37(5):1299-1308. [14]HU Q,QIN K Y.The construction of multi-granularity concept lattices[J].Journal of Intelligent & Fuzzy Systems,2020,39:2783-2790. |
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