Critical Analysis on Open Source LMSs using FCA

Sumangali, K.; Cherukuri, Aswani Kumar

doi:10.4018/ijdet.2013100107

Cited by 9 publications

(3 citation statements)

References 16 publications

(13 reference statements)

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“…A concept lattice illustrates the relations between objects and attributes, describing the relations of generalization and specialization among different concepts. Its Hasse diagram is an important tool of realizing data visualization [11,12,13]. Some definitions are given as follows.…”

Section: A Fcamentioning

confidence: 99%

A New Context Information Selecting Method

2015

2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics

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This paper proposes a new method of selecting context information. The method utilizes ontologies and Formal Concept Analysis to require the context information users need. It can be applied in context search engines and other aspects regarding context-aware technology. The paper also proposes the experiment and assessment of the method to prove this method is really beneficial for context search engines.

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Section: A Fcamentioning

confidence: 99%

A New Context Information Selecting Method

2015

2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics

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“…Details of these applications are available in the literature (Carpineto & Romano, 2004;Priss, 2006). The major areas of its applications are (Wille, 1992;Ganter & Wille, 1999): Knowledge Extraction, Information Restructuring and Classification, Data Mining (Kumar & Sumangali, 2012;Sumangali & Kumar, 2013), Data Visualization, Decision Making. FCA is also used in numerous fields involving the above tasks.…”

Section: Origin and Growth Of Fcamentioning

confidence: 99%

A comprehensive overview on the foundations of formal concept analysis

Sumangali

Cherukuri²

2017

Knowledge Management &Amp; E-Learning: An International Journal

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The immersion of voluminous collection of data is inevitable almost everywhere. The invention of mathematical models to analyse the patterns and trends of the data is an emerging necessity to extract and predict useful information in any Knowledge Discovery from Data (KDD) process. The Formal Concept Analysis (FCA) is an efficient mathematical model used in the process of KDD which is specially designed to portray the structure of the data in a context and depict the underlying patterns and hierarchies in it. Due to the huge increase in the application of FCA in various fields, the number of research and review articles on FCA has raised to a large extent. This review differs from the existing ones in presenting the comprehensive survey on the fundamentals of FCA in a compact and crisp manner to benefit the beginners and its focuses on the scalability issues in FCA. Further, we present the generic anatomy of FCA apart from its origin and growth at a primary level.

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“…FCA has a wide range of applications in a variety of fields. Such as data mining [3], information retrieval [4], neural networks [5], [6], ontology engineering [7]- [9], reliability engineering [10], and social networks [11]- [16]. Make use of FCA modelling techniques in order to capture knowledge.…”

Section: Introductionmentioning

confidence: 99%

Survey on attribute and concept reduction methods in formal concept analysis

Alwersh

Kovács

2023

IJEECS

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Formal concept analysis (FCA) is now widely recognized as a useful approach for extracting, representing, and analyzing knowledge in various domains. The high computational cost of knowledge processing and the difficulty of visualizing the lattice are two key challenges in practical FCA implementations. Moreover, assessing the finalized built-up lattice may be problematic due to the enormous number of formal concepts and the complexity of their connections. The challenge of constructing concept lattices of adequate size and structure to convey high-importance context features remains a significant FCA aim. In the literature, various strategies for concept lattice reduction have been presented. In this work, we suggest a categorization of reduction methods for concept lattice based on three main categories: context pre-processing, non-essential distinctions elimination, and concept filtration, whereby using FCA-based analysis, the most important methods in the literature are analyzed and compared based on six pillars: the preliminary step of the reduction process, domain expert, changing the original data structure, final concept lattice, quality of reduction, and category of reduction method.

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Critical Analysis on Open Source LMSs using FCA

Cited by 9 publications

References 16 publications

A New Context Information Selecting Method

A New Context Information Selecting Method

A comprehensive overview on the foundations of formal concept analysis

Survey on attribute and concept reduction methods in formal concept analysis

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