Recently Formal Concept Analysis (FCA), a mathematical framework based on partial ordering relations has become popular for knowledge representation and reasoning. Further this framework is extended as Fuzzy FCA, Rough FCA, etc. to deal with practical applications. There are investigations in the literature applying FCA for Information Retrieval (IR) applications. The objective of this paper is to apply Fuzzy FCA approach for IR. While adopting Fuzzy FCA, we follow a fast algorithm to generate the fuzzy concepts rather than classical algorithms that are based on residuated methods. Further we follow an approach that retrieves the relevant documents even during absence of exact match of the keywords. © Springer International Publishing Switzerland 2015.

Aswani Kumar Cherukuri

Department of Information Technology

School of Information Technology and Engineering

Vellore Campus

Mouliswaran S.C

Amriteya P

Arun S.R.

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

Fuzzy Formal Concept Analysis Approach for Information Retrieval

Advances in Intelligent Systems and Computing Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015)

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

Pattern association is one among the ways through which human brain stores and recalls information. From the literature, it is evident that cognitive abilities of human brain such as learning, memorizing, recalling and updating of information are performed via concepts and their connections. In this work we have made use of Formal Concept Analysis (FCA), a mathematical framework for data and knowledge processing, to represent memories and to perform some of the cognitive functions of human brain. In particular, we model the functionalities of bidirectional associative memories. The proposed model can learn, memorize the learnt information, bi-directionally recall the information that is associated with the presented cue with the help of object-attribute relations that exists in the scenario and update the knowledge when there is a change in the considered scenario. Also when a noisy cue is given, the model is capable of recalling the most closely associated pattern by exploiting the concept hierarchy principle of FCA. Similarly, when a new information is presented on a learnt scenario, the proposed model can update its knowledge by avoiding the need to re-learn scenario. We illustrate the proposed model with a case study and validate with experiments on few real world datasets. © 2015 Elsevier B.V. All rights reserved.

Biologically Inspired Cognitive Architectures

Formal concept analysis approach to cognitive functionalities of bidirectional associative memory

Formal Concept Analysis (FCA) is a mathematical framework for knowledge processing tasks. FCA has been successfully incorporated into fuzzy setting and its extension (interval- valued fuzzy set) for handling vagueness and impreciseness in data. However, the analysis in such settings is restricted to unipolar space. Recently, some applications of bipolar information are shown in bipolar fuzzy graph, lattice theory as well as in FCA. The adequate analysis of bipolar information using FCA requires incorporation of bipolar fuzzy set and an appropriate lattice structure. For this purpose, we propose an algorithm for generating the bipolar fuzzy formal concepts, a method for (α β)-cut of bipolar fuzzy formal context and its implications with illustrative examples. © 2014 Elsevier Inc. All rights reserved.

Information Sciences

Bipolar fuzzy graph representation of concept lattice

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.

Applied Artificial Intelligence

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	Data powered by TypesetAdvances in Intelligent Systems and Computing Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015)
Publisher	Data powered by TypesetSpringer International Publishing
ISSN	2194-5357
Open Access	0