The difficult job in association rules is to identify the frequent item sets immersed into the huge collection of data. The association rules can be discovered using Formal Concept Analysis (FCA). Several contexts often contain large number of rules and hence interesting rules are required to be determined. With this objective, this paper proposes a method for determining interesting rules in FCA involving many-valued contexts based on Shannon's information entropy (IE) theory. For this purpose we define a gain-lift measure on association rules. The proposed method is illustrated by means of an example available from the field of medical diagnosis. © 2014 IEEE.

Sumangali K

Department of Information Technology

School of Information Technology and Engineering

Vellore Campus

Aswani Kumar Cherukuri

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

Determination of interesting rules in FCA using information gain

2014 First International Conference on Networks & Soft Computing (ICNSC2014)

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

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

Formal Concept Analysis (FCA), in which data is represented as a formal context, offers a framework for Association Rules Mining (ARM) by handling functional dependencies in the data. However, with the size of the formal context, the number of rules grows exponentially. In this article, we apply Fuzzy K-Means clustering on the data set to reduce the formal context and FCA on the reduced data set for mining association rules. With experiments on two real-world healthcare data sets, we offer the evidence for performance of FKM-based FCA in mining association rules. Copyright © 2012 Taylor &amp; Francis Group, LLC.

FUZZY CLUSTERING-BASED FORMAL CONCEPT ANALYSIS FOR ASSOCIATION RULES MINING

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 Typeset2014 First International Conference on Networks & Soft Computing (ICNSC2014)
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Open Access	0