Knowledge Discovery and Rule Extraction Based on Heterogeneous Network Linguistic Formal Context
SHA Liwei1, YANG Zheng1, LIU Hongping2, ZOU Li1
1. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101; 2. School of Science, Shandong Jianzhu University, Jinan 250101
Abstract：One of the research hotspots is how to handle data with complex relationships under the uncertainty environment. The network formal context combines complex network analysis and formal concept analysis to provide an effective mathematical tool for knowledge discovery of complex relational data. In this paper, the heterogeneous network linguistic formal context is firstly proposed based on the heterogeneity of network structure. The heterogeneous network contains a subjective network given by experts and an objective network mined by the features of objects. Then, global and local heterogeneous network language concepts are obtained by considering the connectivity of the network, and the algorithms for global and local connectivity knowledge discovery in heterogeneous networks are provided. Finally, an association rule extraction model is constructed based on the heterogeneous network linguistic formal context, and the rationality and effectiveness of knowledge discovery and rule extraction are verified by examples.
沙立伟, 杨政, 刘红平, 邹丽. 基于异构网络语言形式背景的知识发现及规则提取[J]. 模式识别与人工智能, 2024, 37(5): 469-478.
SHA Liwei, YANG Zheng, LIU Hongping, ZOU Li. Knowledge Discovery and Rule Extraction Based on Heterogeneous Network Linguistic Formal Context. Pattern Recognition and Artificial Intelligence, 2024, 37(5): 469-478.
[1] WILLE R. Restructuring Lattice Theory: An Approach Based on Hie-rarchies of Concepts // Proc of the 7th International Conference on Formal Concept Analysis. Berlin, Germany: Springer, 2009: 314-339. [2] MEDINA J, NAVAREÑO P, RAMÍREZ-POUSSA E. Knowledge Im-plications in Multi-adjoint Concept Lattices // CORNEJO M E, KÓCZY L T, MEDINA-MORENO J, et al., eds. Computational Intelligence and Mathematics for Tackling Complex Problems 2. Berlin, Germany: Springer, 2022: 155-161. [3] HAO F, YANG Y X, MIN G Y, et al. Incremental Construction of Three-Way Concept Lattice for Knowledge Discovery in Social Networks. Information Sciences, 2021, 578: 257-280. [4] BAYHAN S. Textural Dependency and Concept Lattices. Internatio-nal Journal of Approximate Reasoning, 2021, 136: 36-65. [5] LI J H, HUANG C C, QI J J, et al. Three-Way Cognitive Concept Learning via Multi-granularity. Information Sciences, 2017, 378: 244-263. [6] YAN E L, YU C G, LU L M, et al. Incremental Concept Cognitive Learning Based on Three-Way Partial Order Structure. Knowledge-Based Systems, 2021, 220. DOI: 10.1016/j.knosys.2021.106898. [7] HU Q, QIN K Y, YANG H, et al. A Novel Approach to Attribute Reduction and Rule Acquisition of Formal Decision Context. App-lied Intelligence, 2023, 53(11): 13834-13851. [8] WU W Z, LEUNG Y, MI J S. Granular Computing and Knowledge Reduction in Formal Contexts. IEEE Transactions on Knowledge and Data Engineering, 2008, 21(10): 1461-1474. [9] WANG S N, WEI L, LI Q. Construct New Lattices Based on Rough Set Theory // Proc of the 2nd International Conference on Information Science and Engineering. Washington, USA: IEEE,2010: 5013-5016. [10] ZHI H L, QI Z H, LI Y N. An Efficient Conflict Analysis Method Based on Splitting and Merging of Formal Contexts. Information Sciences, 2024, 661. DOI: 10.1016/j.ins.2024.120154. [11] ZADEH L A. Fuzzy Sets. Information and Control, 1965, 8(3): 338-353. [12] ZADEH L A. The Concept of a Linguistic Variable and Its Applica-tion to Approximate Reasoning-III. Information Sciences, 1975, 9(1): 43-80. [13] HERRERA F, HERRERA-VIEDMA E, VERDEGAY J L. A Mo-del of Consensus in Group Decision Making under Linguistic Asse-ssments. Fuzzy Sets and Systems, 1996, 78(1): 73-87. [14] XU Z S. On Generalized Induced Linguistic Aggregation Operators. International Journal of General Systems, 2006, 35(1): 17-28. [15] ZOU L, PANG K, SONG X Y, et al. A Knowledge Reduction App-roach for Linguistic Concept Formal Context. Information Sciences, 2020, 524: 165-183. [16] PANG K,MARTÍNEZ L, LI N, et al. A Concept Lattice-Based Expert Opinion Aggregation Method for Multi-attribute Group Decision-Making with Linguistic Information. Expert Systems with App-lications, 2024, 237. DOI: 10.1016/j.eswa.2023.121485. [17] YU G L, CUI Z W, YUAN Q F. Image Segmentation Method Based on Improved PSO Optimized FCM Algorithm and Its Application. Journal of Computers, 2023, 34(2). DOI: 10.53106/199115992023043402001. [18] YANG Y, WEN Z C. Image Segmentation Based on Super-Pixel with Neighborhood Constrained and Multi-level FCM Clustering. Journal of Computational Methods in Sciences and Engineering, 2022, 22(4): 1117-1130. [19] PANG Y, SHI M L, ZHANG L Y, et al. PR-FCM: A Polynomial Regression-Based Fuzzy C-means Algorithm for Attribute-Associa-ted Data. Information Sciences, 2022, 585: 209-231. [20] 沈浩,王士同. 按风格划分数据的模糊聚类算法.模式识别与人工智能, 2019, 32(3): 204-213. (SHEN H, WANG S T. A Fuzzy Style Clustering Algorithm on Stylistic Data. Pattern Recognition and Artificial Intelligence, 2019, 32(3): 204-213.) [21] 祁宏宇,吴小俊,王士同,等. 一种协同的FCPM模糊聚类算法.模式识别与人工智能, 2010, 23(1): 120-126. (QI H Y, WU X J, WANG S T, et al. Collaborative FCPM Fuzzy Clustering Algorithm. Pattern Recognition and Artificial Intelligence, 2010, 23(1): 120-126. ) [22] ZHAO J X, WANG Y H, GAO P T, et al. Asymptotical Tracking Control for the Complex Network Based on the Dynamic Topology. ISA Transactions, 2024, 148: 105-113. [23] 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: 5126-5145. [24] 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.) [25] 庞阔,陈思琪,宋笑迎,等. 基于粒计算的语言概念决策形式背景分析.山东大学学报(工学版), 2018, 48(6): 74-81. (PANG K, CHEN S Q, SONG X Y, et al. Linguistic Concept Formal Decision Context Analysis Based on Granular Computing. Journal of Shandong University(Engineering Science), 2018, 48(6): 74-81.) [26] VERMA K R, TIWARI R, THAKUR S P. Partition Coefficient and Partition Entropy in Fuzzy C Means Clustering. Journal of Scien-tific Research and Reports, 2023, 29(12): 1-6. [27] YU H Y, JIANG L R, FAN J L,et al. A Feature-Weighted Su-ppressed Possibilistic Fuzzy C-means Clustering Algorithm and Its Application on Color Image Segmentation. Expert Systems with App-lications, 2024. DOI: 10.1016/j.eswa.2023.122270. [28] AGRAWAL R, IMIELIŃSKI T, SWAMI A. Mining Association Rules Between Sets of Items in Large Databases. ACM SIGMOD Record, 1993, 22(2): 207-216.
2024, Vol. 37 
