Knowledge level extraction method based on concept lattice and its application

被引：0
作者：
Mao, Hua [1 ,2 ]
Liu, Chang [1 ,2 ]
Yuan, Xiaolei [1 ]
Liu, Chuan [1 ,2 ]
机构：
[1] College of Mathematics and Information Science, Hebei University, Baoding
[2] Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province, Hebei, Baoding
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2024年 / 52卷 / 11期
关键词：
concept lattice; formal concept analysis; Hasse diagram; knowledge level; knowledge level algorithm;
D O I：
10.13245/j.hust.241105
中图分类号：
学科分类号：
摘要：
To solve the problem that the existing algorithm of generating concept lattice could only Output concepts directly, but could not Output each knowledge level and the knowledge contained in the same knowledge level meanwhile, a knowledge level (KL) algorithm was proposed to transform formal context into concept lattice. On a macro level,KL algorithm could visualize the knowledge in the formal context. From the micro point of view, KL algorithm could Output the specific knowledge level and the knowledge contained in each knowledge level. By comparing KL algorithm with Nextclosure algorithm, it is found that KL algorithm can process data faster than Nextclosure algorithm in terms of complexity when the object set in formal context is large, and when the size of the attribute set in the formal context is not large, KL algorithm has the same data processing speed as Nextclosure algorithm. In terms of generating Hasse graph and knowledge level, KL algorithm has absolute advantage. Research results show that KL algorithm can provide richer results under the same formal context, which is conducive to the popularization of concept lattice theory. © 2024 Huazhong University of Science and Technology. All rights reserved.
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页码：37 / 42and92
页数：4255
相关论文
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