Network Formal Concepts Acquisition Based on Equiconcepts
AI Sensen1, WAN Qing1,2, LI Jinhai3,4
1. School of Science, Xi'an Polytechnic University, Xi'an 710048; 2. Institute of Concepts, Cognition and Intelligence, Northwest University, Xi'an 710127; 3. Faculty of Science, Kunming University of Science and Technology, Kunming 650500; 4. Data Science Research Center, Kunming University of Science and Technology, Kunming 650500
Abstract：In the network formal context induced by graph network data, global network formal concepts and local network formal concepts are obtained by introducing the set connectivity on the basis of formal concepts and semiconcepts respectively, and there is a close relationship between the set connectivity and the equiconcepts of the formal context. Therefore, there must be a correlation between the two types of network formal concepts and equiconcepts. In this paper, for network formal contexts, a method for obtaining all connected subsets of the object set is first proposed by means of the equiconcepts,and some properties of the connected sets are characterized through concept-induced operators. Next, a method is presented for deriving the equiconcepts of the subcontext from the equiconcepts of the original formal context. Subsequently, the methods for acquiring global network formal concepts and local network formal concepts are obtained from the equiconcepts of the subcontext. Finally, numerical experiments illustrate the effectiveness and feasibility of the proposed acquisition methods for the two types of network formal concepts.
艾森森, 万青, 李金海. 基于等势概念的网络形式概念获取[J]. 模式识别与人工智能, 2025, 38(5): 412-424.
AI Sensen, WAN Qing, LI Jinhai. Network Formal Concepts Acquisition Based on Equiconcepts. Pattern Recognition and Artificial Intelligence, 2025, 38(5): 412-424.
[1] WILLE R.Restructuring Lattice Theory: An Approach Based on Hierarchies of Concept // REIDEL D, ed. Ordered Sets. Berlin, Germany: Springer, 1982: 445-470. [2] GANTER B, WILLE R.Formal Concept Analysis: Mathematical Foun-dations. Berlin, Germany: Springer, 1999. [3] FRAWLEY W J, PIATETSKY-SHAPIRO G, MATHEUS C J.Know-ledge Discovery in Databases: An Overview. AI Magazine, 1992, 13(3): 57-70. [4] 智慧来,李金海.基于必然属性分析的粒描述.计算机学报, 2018, 41(12): 2702-2719. (ZHI H L, LI J H.Granule Description Based on Necessary Attri-bute Analysis. Chinese Journal of Computers, 2018, 41(12): 2702-2719.) [5] 胡可云,陆玉昌,石纯一.基于概念格的分类和关联规则的集成挖掘方法.软件学报, 2000, 11(11): 1478-1484. (HU K Y, LU Y C, SHI C Y.An Integrated Mining Approach for Classification and Association Rule Based on Concept Lattice. Journal of Software, 2000, 11(11): 1478-1484.) [6] ZHANG Q, SHI C Y, NIU Z D, et al. HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(1): 152-165. [7] KAYTOUE M, KUZNETSOV S O, NAPOLI A, et al. Mining Gene Expression Data with Pattern Structures in Formal Concept Analysis. Information Sciences, 2011, 181(10): 1989-2001. [8] TONELLA P.Using A Concept Lattice of Decomposition Slices for Program Understanding and Impact Analysis. IEEE Transactions on Software Engineering, 2003, 29(6): 495-509. [9] ZHANG W X, WEI L, QI J J.Attribute Reduction Theory and Approach to Concept Lattice. Science in China Series F: Information Sciences, 2005, 48(6): 713-726. [10] 魏玲,曹丽,祁建军,等.形式概念分析中的概念约简与概念特征.中国科学: 信息科学, 2020, 50(12): 1817-1833. (WEI L, CAO L, QI J J, et al. Concept Reduction and Concept Characteristics in Formal Concept Analysis. SCIENTIA SINICA(Informationis), 2020, 50(12): 1817-1833.) [11] PANG J Z, ZHANG B, CHEN M H.A Novel L-Fuzzy Concept Learning via Two-Way Concept-Cognitive Learning and Residuated Implication. International Journal of Fuzzy Systems, 2024, 26(8): 2783-2804. [12] 李金海,米允龙,刘文奇.概念的渐进式认知理论与方法.计算机学报, 2019, 42(10): 2233-2250. (LI J H, MI Y L, LIU W Q.Incremental Cognition of Concepts: Theories and Methods. Chinese Journal of Computers, 2019, 42(10): 2233-2250.) [13] 徐伟华,李金海,折延宏.概念认知学习理论与方法.北京:科学出版社, 2023. (XU W H, LI J H, ZHE Y H.Concept-Cognitive Learning: Theories and Methods. Beijing, China: Science Press, 2023.) [14] 刘文星,范敏,李金海.网络形式背景下的社区划分方法研究.计算机科学与探索, 2021, 15(8): 1441-1449. (LIU W X, FAN M, LI J H.Research on Community Division Method under Network Formal Context. Journal of Frontiers of Computer Science and Technology, 2021, 15(8): 1441-1449.) [15] FAN M, LUO S, LI J H.Network Rule Extraction under the Network Formal Context Based on Three-Way Decision. Applied Inte-lligence, 2023, 53(5): 5126-5145. [16] 范敏,郭瑞欣,李金海.网络决策形式背景下基于因果力的邻域推荐算法.模式识别与人工智能, 2022, 35(11): 977-988. (FAN M, GUO R X, LI J H.Neighborhood Recommendation Al-gorithm Based on Causality Force under Network Formal Decision Con-text. Pattern Recognition and Artificial Intelligence, 2022, 35(11): 977-988.) [17] 闫梦宇,李金海,刘文奇,等.带对象结构信息形式背景的概念知识发现与演化.电子学报, 2023, 51(1): 11-17. (YAN M Y, LI J H, LIU W Q, et al. Conceptual Knowledge Discovery and Evolution in Formal Context with Object Structure Information. Acta Electronica Sinica, 2023, 51(1): 11-17.) [18] YAN M Y, LI J H.Knowledge Discovery and Updating under the Evolution of Network Formal Contexts Based on Three-Way Decision. Information Sciences, 2022, 601: 18-38. [19] HAO F, YAU S S, MIN G Y, et al. Detecting k-Balanced Trusted Cliques in Signed Social Networks. IEEE Internet Computing, 2014, 18(2): 24-31. [20] HAO F, MIN G Y, PEI Z, et al.K-Clique Community Detection in Social Networks Based on Formal Concept Analysis. IEEE Systems Journal, 2017, 11(1): 250-259. [21] HAO F, PEI Z, YANG L T.Diversified Top-k Maximal Clique Detection in Social Internet of Things. Future Generation Computer Systems, 2020, 107: 408-417. [22] 卢开澄,卢华明. 图论及其应用.北京: 清华大学出版社, 1995. (LU K C, LU H M.Graph Theory and Its Application. Beijing, China: Tsinghua University Press, 1995.)
2025, Vol. 38 
