Formal Concept Analysis (FCA) with fuzzy setting has been successfully applied by researchers for data analysis and representation. Reducing the number of fuzzy formal concepts and their lattice structure are addressed as a major issues. In this study, we try to link between interval-valued fuzzy graph and fuzzy concept lattice to overcome from the issue. We show that proposed method reduces the number of fuzzy formal concepts and their lattice structure while preserving specialized and generalized concepts. Proposed link will be useful for the researcher in data analysis and processing. © 2012 IEEE.

Aswani Kumar Cherukuri

Department of Information Technology

School of Information Technology and Engineering

Vellore Campus

Singh P.K

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)

This paper introduces a mathematical model for precise analysis of uncertainty and its fluctuation in the given interval-valued fuzzy attributes. In this regard, a method is introduced for drawing the interval-valued complex lattice and its navigation at user required complex granules with demonstration. © 2019, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing Recent Developments in Machine Learning and Data Analytics

Interval-Valued Complex Fuzzy Concept Lattice and Its Granular Decomposition

Formal concept analysis (FCA) is a mathematical model for data analysis and processing tasks. Recently, FCA has received significant attention for the analysis of data with fuzzy attributes by representing them as a matter of degree in the formal context. In this paper, we concentrated on the analysis of fuzzy contexts (X, Y, R1) and (Z, Y, R2) which share same attribute set (Y). We can observe that, these two contexts can be connected through composition as (X, Z, R3 = R1 ∗ R2), which contains objects set from one context and attribute set from another. In this case, the problem is how to analyse the composed contexts with regard to its object and attribute set with less number of formal concepts using FCA. For this purpose, a method is proposed in this study which provides one context with regard to its object set and another with regard to its attribute set. © 2016 Inderscience Enterprises Ltd.

International Journal of Data Analysis Techniques and Strategies

Analysis of composed fuzzy contexts through projection

Abstract In recent years, FCA has received significant attention from research communities of various fields. Further, the theory of FCA is being extended into different frontiers and augmented with other knowledge representation frameworks. In this backdrop, this paper aims to provide an understanding of the necessary mathematical background for each extension of FCA like FCA with granular computing, a fuzzy setting, interval-valued, possibility theory, triadic, factor concepts and handling incomplete data. Subsequently, the paper illustrates emerging trends for each extension with applications. To this end, we summarize more than 350 recent (published after 2011) research papers indexed in Google Scholar, IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and a few authoritative fundamental papers.

Fulltext

International Journal of Applied Mathematics and Computer Science

A comprehensive survey on formal concept analysis, its research trends and applications

Formal concept analysis (FCA) is a mathematical framework for data analysis and processing tasks. Based on the lattice and order theory, FCA derives the conceptual hierarchies from the relational information systems. From the crisp setting, FCA has been extended to fuzzy environment. This extension is aimed at handling the uncertain and vague information represented in the form of a formal context whose entries are the degrees from the scale [0, 1]. The present study analyzes the fuzziness in a given many-valued context which is transformed into a fuzzy formal context, to provide an insight into generating the fuzzy formal concepts from the fuzzy formal context. Furthermore, considering that a major problem in FCA with fuzzy setting is to reduce the number of fuzzy formal concepts thereby simplifying the corresponding fuzzy concept lattice structure, the current paper solves the problem by linking an interval-valued fuzzy graph to the fuzzy concept lattice. For this purpose, we propose an algorithm for generating the interval-valued fuzzy formal concepts. To measure the weight of fuzzy formal concepts, an algorithm is proposed using Shannon entropy. The knowledge represented by formal concepts using interval-valued fuzzy graph is compared with entropy-based-weighted fuzzy concepts at chosen threshold. © 2015, Springer-Verlag Berlin Heidelberg.

Soft Computing

Knowledge representation using interval-valued fuzzy formal concept lattice

Formal Concept Analysis (FCA) is a mathematical framework that offers conceptual data analysis and knowledge discovery. One of the main issues of knowledge discovery is knowledge reduction. The objective of this paper is to investigate the knowledge reduction in FCA and propose a method based on Non-Negative Matrix Factorization (NMF) for addressing the issue. Experiments on real world and benchmark datasets offer the evidence for the performance of the proposed method. © 2014 International Association for Mathematics and Computers in Simulation (IMACS).

Journal	Data powered by Typeset2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)
Publisher	Data powered by TypesetIEEE
ISSN	21647143
Open Access	0