FCA is a mathematical framework that depicts knowledge derived from the data represented as formal context. The objective of this paper is to apply Formal Concept Analysis (FCA) to analyze the key performance areas (KPA) of faculty of an institute. While constructing the formal context we have considered the faculties as objects and their KPA as attributes. This context is processed using FCA and the knowledge derived is analyzed to measure the performance of the faculty. © 2012 IEEE.

Aswani Kumar Cherukuri

Department of Information Technology

School of Information Technology and Engineering

Vellore Campus

Sumangali 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.

&nbsp;

&nbsp;

VIT University

Performance evaluation of employees of an organization using formal concept analysis

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)

In Formal Concept Analysis (FCA), a concept lattice graphically portrays the underlying relationships between the objects and attributes of an information system. One of the key complexity problems of concept lattices lies in extracting the useful information. The unorganized nature of attributes in huge contexts often does not yield an informative lattice in FCA. Moreover, understanding the collective relationships between attributes and objects in a larger many valued context is more complicated. In this paper, we introduce a novel approach for deducing a smaller and meaningful concept lattice from which excerpts of concepts can be inferred. In existing attribute-based concept lattice reduction methods for FCA, mostly either the attribute size or the context size is reduced. Our approach involves in organizing the attributes into clusters using their structural similarities and dissimilarities, which is commonly known as attribute clustering, to produce a derived context. We have observed that the deduced concept lattice inherits the structural relationship of the original one. Furthermore, we have mathematically proved that a unique surjective inclusion mapping from the original lattice to the deduced one exists.

Journal of Ambient Intelligence and Humanized Computing

Concept Lattice Simplification in Formal Concept Analysis Using Attribute Clustering

The decision process of medical Ophthalmology vary in the degree to which a patient is fit with respect to various factors such as age, spectacle prescription, astigmatic, tear production rate, etc., In the past rough set theory has been used to study such decision processes. But no efficient procedures have been developed to deal with the complicating aspects of medical Ophthalmology. Therefore an attempt has been made to analyze the inconsistencies of the decision making process. In real life many types of contact lenses are in practice and there are several factors that influence the decision as to which type, if any, to suit. The decision support system adopted by an ophthalmologist is defined by the use of information entropy. Information entropy theory has been used in the method of analyzing the objects and attributes of the decision table for reducing its uncertainty. Then, the priorities of the attributes are determined. The results can address the ophthalmologists in their decision making process. Keywords-decision making; information entropy; uncertainty. © 2017 IEEE.

2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC)

An Application of Information Entropy for Ophthalmologists’ Decision Making

Discovering important concepts in formal concept analysis (FCA) is an important issue due to huge number of concepts arising out of complicated contexts. To address this issue, this paper proposes a method for concept compression in FCA, involving many-valued decision context, based on information entropy. The precedence order of attributes is obtained by using entropy theory developed by Shannon. The set of concepts is compressed using the precedence order thus determined. An algorithm namely Entropy based concept compression (ECC) is developed for this purpose. Further, similarity measures between the actual and compressed concepts are examined using the deviance analysis and percentage error calculation on the deviance of input weights of concepts. From the experiments, it is found that the compressed concepts inherit association rules to the maximum extent. © 2017 Taylor &amp; Francis.

Applied Artificial Intelligence

Concept Compression in Formal Concept Analysis Using Entropy-Based Attribute Priority

The objective of this paper is to apply Formal Concept Analysis (FCA) to identify the best open source Learning Management System (LMS) for an E-learning environment. FCA is a mathematical framework that represents knowledge derived from a formal context. In constructing the formal context, LMSs are treated as objects and their features as attributes. This context is analysed and classified into concepts based on the rules of FCA. The knowledge derived from the concepts and our analyses reveal that Moodle is exceptional, with more features when compared with other LMSs.

International Journal of Distance Education Technologies

Critical Analysis on Open Source LMSs using FCA

Recently, fuzzy set theory in Formal Concept Analysis (FCA) has become an effective tool for knowledge representation and discovery. In this paper we present a method, for reducing the fuzzy relation for a given fuzzy context. The proposed method provides an alternative approach towards reducing the knowledge in the formal context in contrast to existing object, attribute and formal context reduction. Through this method, fuzzy relation can be projected with regards to both objects and attributes and provides two projected fuzzy formal contexts. From these two contexts, we can generate the fuzzy concepts and visualize them in a lattice structure. © 2012 Springer-Verlag.

Mathematical Modelling and Scientific Computation Communications in Computer and Information Science

A Method for Reduction of Fuzzy Relation in Fuzzy Formal Context

Knowledge discovery in data using formal concept analysis and random projections In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.

Fulltext

International Journal of Applied Mathematics and Computer Science

Knowledge discovery in data using formal concept analysis and random projections

In recent times Formal Concept Analysis (FCA), in which the data is represented as a formal context, has gained popularity for Association Rules Mining (ARM). Application of ARM in health care datasets is challenging and a highly rewarding problem. However, datasets in the medical domain are of high dimension. As the dimensionality of dataset increases, size of the formal context as well as complexity of FCA based ARM also increases. To handle the problem of high dimensionality and mine the associations, we propose to apply Singular Value Decomposition (SVD) on the dataset to reduce the dimensionality and apply FCA on the reduced dataset for ARM. To demonstrate the proposed method, experiments are conducted on Tuberculosis (TB) and Hypertension (HP) datasets. Results indicate that with fewer concepts, SVD based FCA has achieved the performance of FCA on TB data and performed better than FCA on HP data.

Journal of Biological Systems

MINING ASSOCIATIONS IN HEALTH CARE DATA USING FORMAL CONCEPT ANALYSIS AND SINGULAR VALUE DECOMPOSITION

During the design of concept lattices, complexity plays a major role in computing all the concepts from the huge incidence matrix. Hence for reducing the size of the lattice, methods based on matrix decompositions like SVD are available in the literature. However, SVD computation is known to have large time and memory requirements. In this paper, we propose a new method based on Fuzzy K-Means clustering for reducing the size of the concept lattices. We demonstrate the implementation of proposed method on two application areas: information retrieval and information visualization. © 2009 Elsevier Ltd. All rights reserved.

Journal	Data powered by TypesetInternational Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)
Publisher	Data powered by TypesetIEEE
Open Access	0