Applications of Fuzzy Technology in Business Intelligence

Fuzzy Set Theory has been developed during the last decades to a demanding mathematical theory. There exist more than 50,000 publications in this area by now. Unluckily the number of reports on applications of fuzzy technology has become very scarce. The reasons for that are manifold: Real applications are normally not single-method-applications but rather complex combinations of different techniques, which are not suited for a publication in a journal. Sometimes considerations of competition my play a role, and sometimes the theoretical core of an application is not suited for publication. In this paper we shall focus on applications of fuzzy technology on real problems in business management. Two versions of fuzzy technology will be used: Fuzzy Knowledge based systems and fuzzy clustering. It is assumed that the reader is familiar with basic fuzzy set theory and the goal of the paper is, to show that the potential of applying fuzzy technology in management is still very large and hardly exploited so far.

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Application of Fuzzy DEMATEL in Risks Evaluation of Knowledge-Based Networks

Journal of Optimization ◽

10.1155/2013/913467 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

M. Abbasi ◽

R. Hosnavi ◽

B. Tabrizi

Keyword(s):

Risk Factors ◽

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

New Products ◽

Linguistic Variables ◽

Fuzzy Dematel ◽

Knowledge Based ◽

Wide Range ◽

Do So

Developing new products has received much attention within the last decades. This issue can be highlighted for strategic innovations, in particular. Recently, knowledge-based networks have been introduced in order to facilitate the affair of transforming knowledge into commercial products which can be regarded as a set of research centers, universities, knowledge intermediaries, customers, and so forth. However, there is a wide range of risk factors that are liable to affect the chain performance. Hence, this paper aims to consider the most influencing criteria that can play a more significant role in achievements of such networks. To do so, DEMATEL has been applied to take the relationships between the risk factors into account. Moreover, fuzzy set theory has been utilized in order to deal with the linguistic variables. Finally, the most important factors are identified and their relations are determined.

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A GENERALIZED APPROACH TO POSSIBILISTIC CLUSTERING ALGORITHMS

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488507004650 ◽

2007 ◽

Vol 15 (supp02) ◽

pp. 117-138 ◽

Cited By ~ 11

Author(s):

JIAN ZHOU ◽

CHIH-CHENG HUNG

Keyword(s):

Set Theory ◽

Fuzzy Clustering ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Clustering Algorithms ◽

Infinite Family ◽

Real Data ◽

Point Of View ◽

New Family ◽

Possibilistic Clustering

Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approaches, in this paper, a general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. This new method is then used to develop a generalized approach of possibilistic clustering to obtain an infinite family of generalized possibilistic clustering algorithms. We also point out that the existing possibilistic clustering algorithms are members of this family. Following that, some specific possibilistic clustering algorithms in the new family are demonstrated by real data experiments, and the results show that these new proposed algorithms are efficient for clustering and easy for computer implementation.

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A Short Survey on Genetic Sequences, Chou’s Pseudo Amino Acid Composition and its Combination with Fuzzy Set Theory

The Open Bioinformatics Journal ◽

10.2174/1875036201307010041 ◽

2013 ◽

Vol 7 (1) ◽

pp. 41-48 ◽

Cited By ~ 37

Author(s):

D.N. Georgiou ◽

T.E. Karakasidis ◽

A.C. Megaritis

Keyword(s):

Amino Acid ◽

Sequence Analysis ◽

Amino Acid Composition ◽

Acid Composition ◽

Set Theory ◽

Fuzzy Clustering ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Pseudo Amino Acid Composition ◽

Genetic Sequences

The study of genetic sequences is of great importance in biology and medicine. Sequence analysis and taxonomy are two major fields of application of bioinformatics. In this survey, we present results concerning genetic sequences and Chou's pseudo amino acid composition as well as methodologies developed based on this concept along with elements of fuzzy set theory, and emphasize on fuzzy clustering and its application in analysis of genetic sequences.

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Decision Making Problem for Medical Diagnosis Using Hexagonal Fuzzy Number Matrix

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.34.19410 ◽

2018 ◽

Vol 7 (3.34) ◽

pp. 660

Author(s):

S Sandhiy ◽

K Selvakumari

Keyword(s):

Decision Making ◽

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Medical Diagnosis ◽

Vital Role ◽

Diagnostic Model ◽

Knowledge Based ◽

Medical Diagnostic ◽

Membership Matrix

Fuzzy set theory plays a vital role in medical fields. There are varieties of models involving fuzzy matrices to deal with different complicated aspects of medical diagnosis. Fuzzy set theory is highly suitable and applicable for developing knowledge based system in medicine for the tasks of medical findings. The field of medicine and decision making are the most fruitful andinteresting area of applications of fuzzy set theory. In this paper, we have applied the notion of Hexagonal fuzzy membership matrix in a medical diagnostic model. The advantage of this model is, if the patient-matrices are known, then it is possible to find which patient is suffering from what kind of disease. Most probably the fuzzy decision model in which overall ranking or ordering of different fuzzy sets are determined by using comparison matrix.

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A fuzzy clustering-based method for scenario analysis in strategic planning: The case of an Asian pharmaceutical company

South African Journal of Business Management ◽

10.4102/sajbm.v39i3.564 ◽

2008 ◽

Vol 39 (3) ◽

pp. 21-31 ◽

Cited By ~ 1

Author(s):

M. S. Pishvaee ◽

M. Fathi ◽

F. Jolai

Keyword(s):

Strategic Planning ◽

Pharmaceutical Company ◽

Set Theory ◽

Fuzzy Clustering ◽

Fuzzy Set ◽

Scenario Analysis ◽

Fuzzy Set Theory ◽

Uncertain Parameters ◽

Competitive Advantages ◽

Lack Of Information

In today’s rapid changing market situations, many nations and companies try to keep or make better their situation and gain more market share by creating competitive advantages. Because of growing number of uncertain parameters in the environment and lack of information about the future, the strategic choice has become very complex and critical. One of the popular tools for solving the problem is scenario analysis. In this paper based on fuzzy clustering we propose a method for building, analyzing and ranking the possible scenarios. To cope with the issue of uncertain parameters of the environment in strategic planning, we use the concept of fuzzy set theory to enhance the proposed method. Finally the performance of the proposed method is illustrated in a strategic planning case in a pharmaceutical company.

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Fuzzy Logic and Its Application in Football Team Ranking

The Scientific World JOURNAL ◽

10.1155/2014/291650 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Wenyi Zeng ◽

Junhong Li

Keyword(s):

Fuzzy Logic ◽

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Knowledge Based ◽

Fuzzy Clustering Analysis ◽

Football Team ◽

Fuzzy Logic Method ◽

Selection For ◽

Sports Data

Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in physical education for tasks such as the selection for athletes, the evaluation for different training approaches, the team ranking, and the real-time monitoring of sports data. In this paper, we use fuzzy set theory and apply fuzzy clustering analysis in football team ranking. Based on some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example,T7,T3,T1,T9,T10,T8,T11,T12,T2,T6,T5,T4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range.

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