Behavior of the Concept Lattice Reduction to visualizing data after Using Matrix Decompositions

Snášel, Václav; Abdulla, Hussam M. Dahwa; Polovincak, Martin

doi:10.1109/iit.2007.4430480

Cited by 20 publications

(5 citation statements)

References 4 publications

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“…There are also various simplification methods which does not simply follow the typical grouping-merging flow. The research in [16] apply several matrix decomposition methods, including the previously mentioned SVD, semi discrete matrix decomposition (SDD) and non-negative matrix factorization (NMF), for reducing the rank of the binary matrix converted from the formal context. However, unlike the method proposed in [3], they do not merge any objects or attributes after decomposition.…”

Section: Other Methodsmentioning

confidence: 99%

“…However, unlike the method proposed in [3], they do not merge any objects or attributes after decomposition. Instead, they directly approximate the reconstructed low-ranked matrix back into a binary matrix and use it as the final result for the reduction [16,17]. Additionally, a novel method called Adaptive Evolutionary Clustering Algorithm Star (Adaptive ECA*) is proposed in [10], which is specially designed for the task of deriving concept hierarchies from text.…”

Section: Other Methodsmentioning

confidence: 99%

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Analyzing the Reliability of the Grouping-Based Concept Lattice Reductions and a Method for Improving It

Peng

Yamamoto

2022

Preprint

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Formal concept analysis (FCA) is an important task for analyzing rela-tional data as well as extracting formalized knowledge from them. It has a wide range of practical applications, while a problem is frequently encountered that the extracted knowledge, i.e., the concept lattice may be too complicated for further processing and/or analysis. To reduce the concept lattice, various branches of techniques have been proposed and one of the most preferred categories of methods is the grouping-based simplification methods which aim at reducing the volume of the concept lattice by removing the redundant objects/attributes. However, we figure out that these methods may introduce too much information distortion to the context and the concept lattice, making the reduction result unreliable. To identify and overcome these problems, we propose a method for analyzing the reliability of a reduction in terms of context fidelity and lattice fidelity as well as a novel grouping-based concept lattice reduction method using an integer linear programming model. We conduct experiments on several data sets to prove that our method works in different cases and can produce more reliable reductions.

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Section: Other Methodsmentioning

confidence: 99%

Section: Other Methodsmentioning

confidence: 99%

Analyzing the Reliability of the Grouping-Based Concept Lattice Reductions and a Method for Improving It

Peng

Yamamoto

2022

Preprint

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“…The literature describes a variety of techniques for controlling the complexity and size of formal contexts, formal concepts, concept lattices, and implications. To enhancing FCA scalability there are popular research techniques include iceberg concept lattices [10], matrix decompositions [42], conceptual scaling for many-valued contexts [43], the reduction of the concept lattices based on rough set [44], and other. In [39], the authors divided concept lattice reduction techniques into three categories.…”

Section: Current Issues and Research Directionsmentioning

confidence: 99%

Fuzzy formal concept analysis: approaches, applications and issues

Alwersh

Kovács

2022

Comput Sci Inf Technol

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Formal concept analysis (FCA) is today regarded as a significant technique for knowledge extraction, representation, and analysis for applications in a variety of fields. Significant progress has been made in recent years to extend FCA theory to deal with uncertain and imperfect data. The computational complexity associated with the enormous number of formal concepts generated has been identified as an issue in various applications. In general, the generation of a concept lattice of sufficient complexity and size is one of the most fundamental challenges in FCA. The goal of this work is to provide an overview of research articles that assess and compare numerous fuzzy formal concept analysis techniques which have been suggested, as well as to explore the key techniques for reducing concept lattice size. as well as we'll present a review of research articles on using fuzzy formal concept analysis in ontology engineering, knowledge discovery in databases and data mining, and information retrieval.

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“…In fact, if an object, attribute or incidence can be removed or changed in a way that the resulting concept lattice is isomorphic to the original one, then such elements are redundant. Among the most important models based on this technique are (ASWANI KUMAR and SRINIVAS, 2010;Codocedo et al, 2011;Snasel et al, 2007).…”

Section: Redundant Information Removalmentioning

confidence: 99%

Arabic language and knowledge reduction in formal contexts

Sahmoudi

2019

IJCSM

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Formal concept analysis (FCA) is a mathematical tool that offers the conceptual data for knowledge representation, extraction and analysis with applications in the different areas. One of the main problems of FCA is the computational cost due to the large number of formal concepts generated. The objective of this paper is to address this problem by reducing the knowledge in formal contexts, we propose to use a linguistic approach, and we apply this latter for Arabic language. Objective performance evaluations are conducted to assess the efficiency of our proposed method.

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Behavior of the Concept Lattice Reduction to visualizing data after Using Matrix Decompositions

Cited by 20 publications

References 4 publications

Analyzing the Reliability of the Grouping-Based Concept Lattice Reductions and a Method for Improving It

Analyzing the Reliability of the Grouping-Based Concept Lattice Reductions and a Method for Improving It

Fuzzy formal concept analysis: approaches, applications and issues

Arabic language and knowledge reduction in formal contexts

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