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Data Mining of Gene Expression Data by Fuzzy and Hybrid Fuzzy Methods
IEEE Transactions on Information Technology in Biomedicine, 2010Microarray studies and gene expression analysis have received tremendous attention over the last few years and provide many promising avenues toward the understanding of fundamental questions in biology and medicine. Data mining of these vasts amount of data is crucial in gaining this understanding.
Gerald Schaefer, Tomoharu Nakashima
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2013
Fuzzy systems can be found in nearly all industrial branches, e.g. automobile, control engineering, finance, medicine, logistics, telecommunications. Their advantage is their inherent simplicity. Fuzzy rule-based models often turn out to be useful and easily understandable in many real-world applications.
Rudolf Kruse +2 more
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Fuzzy systems can be found in nearly all industrial branches, e.g. automobile, control engineering, finance, medicine, logistics, telecommunications. Their advantage is their inherent simplicity. Fuzzy rule-based models often turn out to be useful and easily understandable in many real-world applications.
Rudolf Kruse +2 more
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Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 2002
Providing efficient query processing in database systems is one step in gaining acceptance of such systems by end users. We propose several techniques for indexing fuzzy sets in databases to improve the query evaluation performance. Three of the presented access methods are based on superimposed coding, while the fourth relies on inverted files.
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Providing efficient query processing in database systems is one step in gaining acceptance of such systems by end users. We propose several techniques for indexing fuzzy sets in databases to improve the query evaluation performance. Three of the presented access methods are based on superimposed coding, while the fourth relies on inverted files.
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Fuzzy clustering model for fuzzy data
Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, 2002In a clustering problem in which the observations of the objects are given by the values involving vagueness, the ordinary fuzzy clustering methods are not available. In this paper, these data are treated as fuzzy data which are defined by convex and normal fuzzy sets (CNF sets), and a new fuzzy clustering model for the fuzzy data is proposed.
M. Sato, Y. Sato
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Fuzzy Clustering of Intuitionistic Fuzzy Data
2012In the paper a new method of fuzzy clustering basing on fuzzy features is presented. Objects are described by set of features with intutionistic fuzzy values. Generally, the method uses the concept of modified fuzzy c-means procedure applied to intuitionistic fuzzy data which describes the features. New distance measure between data and cluster centers
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Robust Fuzzy Clustering with Fuzzy Data
2005Proposed method of clustering is based on modified fuzzy c-means algorithm. In the paper features of input data are considered as linguistic variables. Any feature is described by set of fuzzy numbers. Thus, any input data representing a feature is a fuzzy number. The modified method allows finding the appropriate number of classes.
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Novel fuzzy clustering algorithm for fuzzy data
2015 Eighth International Conference on Contemporary Computing (IC3), 2015This paper presents a new fuzzy clustering algorithm for fuzzy numbers, called the weight fuzzy c-means (WFCM) clustering based on distance function [1]. We first discuss the conventional FCM algorithm for crisp data with brief overview of fuzzy set theory related to the problem at hand and indicate the disparity in the existing approaches of ...
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On possibility analysis of fuzzy data
Fuzzy Sets and Systems, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Miin-Shen Yang, Man-Chun Liu
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2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2013
This paper proposes a method for fuzzy data processing based on Mamdani's fuzzy inference method. The implementation of this method is divided into phases of training and exploitation, which reduces the number of operations during fuzzy data processing and improves its performance.
Lesia Dubchak +3 more
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This paper proposes a method for fuzzy data processing based on Mamdani's fuzzy inference method. The implementation of this method is divided into phases of training and exploitation, which reduces the number of operations during fuzzy data processing and improves its performance.
Lesia Dubchak +3 more
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Multiple regression with fuzzy data
Fuzzy Sets and Systems, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andrzej Bargiela +2 more
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