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Fuzzy C-Means Based DNA Motif Discovery
2008In this paper, we examined the problem of identifying motifs in DNA sequences. Transcription-binding sites, which are functionally significant subsequences, are considered as motifs. In order to reveal such DNA motifs, our method makes use of Fuzzy clustering of Position Weight Matrix.
Karabulut M., Ibrikci T.
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Rough C-means and Fuzzy Rough C-means for Colour Quantisation
Fundamenta Informaticae, 2012Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best ...
Schaefer, G. +4 more
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Generalizations of Fuzzy c-Means and Fuzzy Classifiers
2016Different methods of generalized fuzzy c-means having cluster size variables and cluster covariance variables are compared, which include Gustafson-Kessel’s method, Ichihashi’s method of KL-information, and Yang’s method of fuzzified maximum likelihood.
Sadaaki Miyamoto +2 more
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A fuzzy microaggregation algorithm using fuzzy c-means
2015Masking methods are used in data privacy to avoid the disclosure of sensitive information. Microaggregation is a perturbative masking method that has been proven effective. Data masked using microaggregation can be attacked when the intruder has information of the masking method and the parameters used.
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A review of fuzzy AHP methods for decision-making with subjective judgements
Expert Systems With Applications, 2020Yan Liu, Claudia M Eckert
exaly
Quantification of “fuzzy” chemical concepts: a computational perspective
Chemical Society Reviews, 2012Jérôme F Gonthier +2 more
exaly