An Efficient Algorithm for Decreasing the Granularity Levels of Attributes in Formal Concept Analysis

Wan, Ying; Zou, Ligeng

doi:10.1109/access.2019.2892016

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Zhang et al [28] put forward the concept lattice generation algorithm when attributes in the formal context are reduced, but this algorithm suffers a running time. The Fold [29] and Unfold [30] algorithms were proposed to zoom out to decrease or increase the granularity levels of attributes in FCA without rebuilding the new lattice, and they provided classification and preprocessing procedures that can improve algorithm efficiency. Yang et al [31] proposed an algorithm for attribute-incremental formal context.…”

Section: A Concept Lattice Generationmentioning

confidence: 99%

Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach

Yang

Hao

Pang

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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The booming of Social Internet of Things (SIoT) has witnessed the significance of graph mining and analysis for social network management. Online Social Networks (OSNs) can be efficiently managed by monitoring users' behaviors within a cohesive social group represented by a maximal clique. They can further provide valued social intelligence for their users. Maximal Cliques Problem (MCP) as a fundamental problem in graph mining and analysis is to identify the maximal cliques in a graph. Existing studies on MCP mainly focus on static graphs, with less attention on the detection and dynamic evolution of maximal cliques in OSNs. To fill this gap, we adopt the Formal Concept Analysis (FCA) theory to represent and analyze social networks. We then develop two novel formal concepts generation algorithms, termed Add-FCA and Dec-FCA, that can be applicable to OSNs for detecting the maximal cliques and characterizing the dynamic evolution process of maximal cliques in OSNs. Extensive experimental results are conducted to investigate and demonstrate the correctness and effectiveness of the proposed algorithms. The results reveal that our algorithms can efficiently capture and manage the evolutionary patterns of maximal cliques, including unchanged, changed, added, and vanished maximal cliques in OSNs, and a quantitative relation among them is presented. In addition, an illustrative example is presented to verify the usefulness of the proposed approach.

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Section: A Concept Lattice Generationmentioning

confidence: 99%

Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach

Yang

Hao

Pang

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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show abstract

“…Since its inception, FCA has been applied in software engineering, machine learning, knowledge discovery, information retrieval, and other fields [3]- [5]. New research results on the FCA-related algorithms [6]- [8] have recently made a wider application of FCA in the large-scale data processing…”

Section: Introductionmentioning

confidence: 99%

A Similarity Measure in Formal Concept Analysis Containing General Semantic Information and Domain Information

Wang

Cai

et al. 2020

IEEE Access

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Formal concept analysis (FCA) gets into good graces by increasing big data scientists due to its unique advantages. Concept similarity measurement is the key to the FCA-based application. Most of the previous methods are based on set theory and less concerned with semantic information, whereas those methods focusing on semantic information usually rely on ontologies or knowledge bases to obtain the relevant semantic knowledge. However, it is difficult for knowledge methods to obtain domain knowledge in formal contexts (datasets), which are not suited well for domain text data. To tackle these problems, this paper proposes a novel formal concept similarity measure that synthesizes the Semantic information in knowledge bases and Domain information in the formal context (S&D measure). S&D uses word vectors as the representations of words to obtain the semantic information in general knowledge bases while defining novel semantic relations of intent words to obtain the domain information contained in the data itself. It can measure the similarity relation of concepts more comprehensively and precisely, particularly in a domain textual formal context, and it can be implemented automatically and unsupervisedly without any knowledge base, ontology or external corpus. Compared with other related works, experiments show that this method has a better correlation with human judgment.

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“…In addition, two key operations were defined for extracting the common attributes/public objects for a given set of objects/attributes. In recent years, many scholars have successfully integrated FCA with specific tasks such as data mining [15], machine learning [16], [17], [18], knowledge discovery [19], [20], cloud computing [21], complex network [22], [23], [24], [25], and so on.…”

mentioning

confidence: 99%

Overlapping Community Detection Based on Weak Equiconcept

Shi,

Yan,

2024

IEEE Access

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Community discovery refers to the process of searching for clusters in a network that are sparsely connected to other nodes and form dense connections internally. In many real networks, some communities often overlap with each other, meaning that a node may belong to multiple communities simultaneously. Revealing these (overlapping) community structures is an important issue in complex network analysis, as it helps to better analyze the characteristics and organizational structure of the network. Community expansion methods are very important in the study of community detection problems. However, one of the key issues in developing effective community expansion methods is that the position of seed nodes greatly affects the performance of these algorithms, resulting in low robustness of these algorithms. Meanwhile, it is also difficult for them to provide high-quality results for community detection task. To solve the above problem, this paper proposes a seed selection method based on the weak equiconcepts in a network formal context, which integrates the attribute information of nodes during random walk to detect overlapping communities. Specifically, the weak equiconcepts are constructed by establishing a network formal context to obtain seed sets, and an improved PageRank clustering algorithm is used to expand these seed sets to better reveal the overlapping community structure in the network. Experiments show that seed selection is helpful to improving the performance of overlapping community detection algorithms.INDEX TERMS Overlapping community discovery, network formal context, weak equiconcept, random walk I. INTRODUCTION

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An Efficient Algorithm for Decreasing the Granularity Levels of Attributes in Formal Concept Analysis

Cited by 8 publications

References 25 publications

Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach

Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach

A Similarity Measure in Formal Concept Analysis Containing General Semantic Information and Domain Information

Overlapping Community Detection Based on Weak Equiconcept

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