Network rule extraction under the network formal context based on three-way decision

Abstract

Knowledge discovery combined with network structure is an emerging field of network data analysis and mining. Three-way concept analysis is a method that can fit the human mind in uncertain decisions and analysis. In reality, when three-way concept analysis is placed in the background of a network, not only the three-way rules need to be obtained, but also the network characteristic values of these rules should be obtained, which is of great significance for concept cognition in the network. This paper mainly combines complex network analysis with the formal context of three-way decision. Firstly, the network formal context of three-way decision (NFC3WD) is proposed to unify the two studies mentioned above into one data framework. Then, the network weaken-concepts of three-way decision (NWC3WD) and their corresponding sub-networks are studied. Therefore, we can not only find out the network weaken-concepts but also know the average influence of the sub-network, as well as the influence difference within the sub-network. Furthermore, the concept logic of network and the properties of its operators are put forward, which lays a foundation for designing the algorithm of rule extraction. Subsequently, the bidirectional rule extraction algorithm and reduction algorithm based on confidence degree are also explored. Meanwhile, these algorithms are applied to the diagnosis examples of COVID-19 from which we can not only get diagnostic rules, but also know the importance of the population corresponding to these diagnostic rules in the network through network eigenvalues. Finally, experimental analysis is made to show the superiority of the proposed method.

Overlapping Community Detection Based on Weak Equiconcept

“…Definition 4 [27]: A quadruple (U , M , A, I ) is termed a network formal context, where M = {M 1 , M 2 , . .…”

Association rule mining with fuzzy linguistic information based on attribute partial ordered structure

“…As the core data structure of FCA, concept lattice can extract association information from massive data and intuitively represent the hierarchical relationship between concepts [8][9][10][11]. In order to increase users' understanding, researchers have proposed various association rule mining methods based on concept lattice to visualize association rules [12][13][14][15][16][17][18][19][20][21][22]. For instance, Wang et al [20] applied the quantitative concept lattice to obtain frequent itemsets, which method was found quite intuitive and the expression concise.…”