Results 211 to 220 of about 80,067 (268)
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Fuzzy and possibilistic clustering for fuzzy data
Computational Statistics & Data Analysis, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
COPPI, Renato +2 more
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Comments on “Fuzzy data dependencies and implication of fuzzy data dependencies”
Fuzzy Sets and Systems, 2004In this paper we discuss the results given by Wei-Yi Liu in the article entitled “Fuzzy data dependencies and implication of fuzzy data dependencies” (Fuzzy Sets and Systems 92 (1997) 341) and we get some different conclusions.
Dang, Thanh Ha, Tran, Dinh Khang
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Fuzzy Sets and Systems, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jesús Medina 0001, Reinhard Pichler
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Jesús Medina 0001, Reinhard Pichler
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Proceedings of 12th International Conference on Pattern Recognition, 2002
Symmetry is usually viewed as a discrete feature: an object is either symmetric or non-symmetric. Following the view that symmetry is a continuous feature, a continuous symmetry measure (CSM) has been developed to evaluate symmetries of shapes and objects. In this paper the authors extend the symmetry measure to evaluate the imperfect symmetry of fuzzy
Hagit Zabrodsky +2 more
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Symmetry is usually viewed as a discrete feature: an object is either symmetric or non-symmetric. Following the view that symmetry is a continuous feature, a continuous symmetry measure (CSM) has been developed to evaluate symmetries of shapes and objects. In this paper the authors extend the symmetry measure to evaluate the imperfect symmetry of fuzzy
Hagit Zabrodsky +2 more
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Fuzzy data dependencies and implication of fuzzy data dependencies
Fuzzy Sets and Systems, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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INTERPOLATION OF FUZZY DATA BY USING FUZZY SPLINES
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2008In this paper we define a new set of spline functions called “Fuzzy Splines” to interpolate fuzzy data. Numerical examples will be presented to illustrate the differences between of using our spline and other interpolations that have been studied before.
Saeid Abbasbandy +2 more
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Fuzzy data mining: effect of fuzzy discretization
Proceedings 2001 IEEE International Conference on Data Mining, 2002When we generate association rules, continuous attributes have to be discretized into intervals while our knowledge representation is not always based on such discretization. For example, we usually use some linguistic terms (e.g., young, middle age, and old) for dividing our ages into some fuzzy categories.
Hisao Ishibuchi +2 more
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IEEE Transactions on Fuzzy Systems, 2010
Data standardization is a basic task in data analysis when several incommensurable criteria are involved. This paper discusses a data-standardization method for a set of fuzzy numbers that subtracts their minimum from the number to be standardized and divides the result by the difference between their maximum and minimum.
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Data standardization is a basic task in data analysis when several incommensurable criteria are involved. This paper discusses a data-standardization method for a set of fuzzy numbers that subtracts their minimum from the number to be standardized and divides the result by the difference between their maximum and minimum.
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Intuitionistic fuzzy hypothesis testing with fuzzy data
J. Multiple Valued Log. Soft Comput., 2021Summary: Under vague and imprecise environment, data analysis methods are usually integrated with the fuzzy set theory. Intuitionistic methods sets are the extension of ordinary fuzzy sets whose aim is to incorporate the hesitancy of experts into their model.
Gültekin Atalik +3 more
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From fuzzy data analysis and fuzzy regression to granular fuzzy data analysis
Fuzzy Sets and Systems, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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