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How is this formula applied to normalize data? I am reading this paper http://www.ijcte.org/papers/288-L052.pdf

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$x'=(x-X_{min})+X_{min}$ ?

then $x'=x$ ?

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This formula looks like a typo. Min-max normalization is:

$$ \tilde{x}_i = \frac{x_i - x_{min}}{x_{max} - x_{min}}. $$ This linearly transforms data to fit the interval $[0,1]$. The inverse transformation is clearly

$$ x_i = \tilde{x}_i (x_{max} - x_{min}) + x_{min}.$$ The authors seemed to have mixed both formulas in the same expression and you get your result.

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  • $\begingroup$ i need any reference for my thesis please $\endgroup$ – x-rw Jun 29 '18 at 14:19
  • $\begingroup$ I want a reference to quote with bibtex, is there another apart from this docs.google.com/document/d/…? $\endgroup$ – x-rw Jun 29 '18 at 14:38
  • $\begingroup$ You can cite cs.bilkent.edu.tr/~saksoy/papers/prletters01_likelihood.pdf if you really need it. However, such a basic technique is often not cited, it is equivalent to referencing Newton for a derivative or Cauchy for epsilon limits. If what you want to do in compare the performance of this normalization with others, than there might be literature on the subject that interests you, but a reference for the formula seems overkill to me. $\endgroup$ – Ricardo Jul 2 '18 at 7:19

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