Publication Analysis of the Formal Concept Analysis Community

Doerfel, Stephan; Jäschke, Robert; Stumme, Gerd

doi:10.1007/978-3-642-29892-9_12

Cited by 12 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In data mining, this technique has many diverse applications in areas such as bioinformatics (Coste, Garet, Groisillier, Nicolas, & Tonon, 2014;Kaytoue, Kuznetsov, Napoli, & Duplessis, 2011), social network analysis (Obiedkov, 2013), semantic web search (Dubois & Prade, 2012), technology trend monitoring (Lee, Jeon, & Park, 2011), discovery of association rules of web pages (Du & Li, 2010), terrorist threat assessment (Elzinga, Poelmans, Viaene, Dedene, & Morsing, 2010), text adaptation (Dufour-Lussier, Lieber, Nauer, & Toussaint, 2010), and software engineering (He, Rümmer, & Kroening, 2011). More specifically, Doerfel, Jäschke, and Stumme (2012) used the FCA technique to analyse the state of published papers in this field.…”

Section: Formal Concept Analysismentioning

confidence: 99%

MarCHGen: A framework for generating a malware concept hierarchy

et al. 2019

View full text Add to dashboard Cite

Automatic classification of virus instances into a concept hierarchy has been attracting much attention from malware research community. However, it is definitely not a trivial work, because malwares usually come in binary forms whose actions are complicated and obfuscated. Therefore, the typical data mining approaches based on feature extraction are not easily applied. In this paper, we tackle this problem by introducing a framework known as MarCHGen (Malware Concept Hierarchy Generation). In this framework, we first apply virus logical concept analysis, which incorporates formal concept analysis with temporal logic to capture malware behaviours and generalize a virus concept lattice accordingly. Second, we propose an on‐the‐fly conceptual clustering technique to generate a malware concept hierarchy. In the MarCHGen framework, the malware concept hierarchy will be monitored by the prelarge data set management technique to avoid reclustering several times unnecessarily. Our approach has been applied in a real data set of virus, and promising experimental results have been acquired.

show abstract

Section: Formal Concept Analysismentioning

confidence: 99%

MarCHGen: A framework for generating a malware concept hierarchy

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In the field of data mining, this technique has many rich applications such as biological information [29], [30], social network analysis [31], semantic web search [32], coin tracking. technology orientation [33], understanding the association's law [34], assessing terrorist risk [35], document compatibility [36] and software technology [37]. More specifically, [38] used the FCA technique to analyze the publishing status of articles in this field.…”

Section: Sentiment Ontology Enrichment Using Lcamentioning

confidence: 99%

Ontology Enrichment for Aspect-Oriented Sentiment Analysis with Deep Learning Using Logical Concept Analysis

Tho¹

2019

JCP

View full text Add to dashboard Cite

Aspect-oriented sentiment analysis is a problem that have been attracting much attention from the research community. In the past, using ontologies to represent domain knowledge helped increase the performance of this task. Recently, with the rapid development of deep learning techniques, combination of deep learning with ontology has become very promising. A natural approach is to use aspects represented by ontologies to form input vectors s for deep learning models. However, ontologies often only represent concrete concepts, whereas machine learning techniques often focus on latent features. To address this, we propose using Logical Concept Analysis (LCA), an extension of Formal Concept Analysis (FCA), to enrich the ontology by generating more abstract concepts from existing concrete concepts. We have applied this approach to real data and achieved promising results.

show abstract

“…Over the past 30 years, the initial theory of FCA has been combined with and fertilized by other research domains in mathematics, including deception logics and conceptual graphs [44]. To handle the uncertainty and vagueness in data, FCA has been successfully extended with a fuzzy setting, an interval-valued fuzzy setting, possibility theory, a rough setting and triadic concept analysis ( [12], [15], [16], [38], [39], [43] and [53]).…”

Section: Introductionmentioning

confidence: 99%

Graded Galois Lattices and Closed Itemsets

Sotoudeh¹,

Goudarzi²,

Nikoukar³

2021

Preprint

View full text Add to dashboard Cite

The Galois lattice is a graphic method of representing knowledge structures. The first basic purpose in this paper is to introduce a new class of Galois lattices, called graded Galois lattices. As a direct result, one can obtain the notion of graded closed itemsets (sets of items), to extend the definition of closed itemsets. Our second important goal in this paper, is related to set a constructive method, computing the graded formal concepts and graded closed itemsets. We mean by a constructive method, a method that builds up a complete solution from scratch by sequentially adding components to a partial solution until the solution is complete. Besides of computational aspects, our methods in this paper are based on the strong results obtained by special mappings in the realm of domain theory. To reach the fertilized consequences and constructive algorithms, we need to push the study to the structures of Banach lattices.

show abstract

Publication Analysis of the Formal Concept Analysis Community

Cited by 12 publications

References 44 publications

MarCHGen: A framework for generating a malware concept hierarchy

MarCHGen: A framework for generating a malware concept hierarchy

Ontology Enrichment for Aspect-Oriented Sentiment Analysis with Deep Learning Using Logical Concept Analysis

Graded Galois Lattices and Closed Itemsets

Contact Info

Product

Resources

About