Page 50 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u062950neural networks or distance measurements such as nearest-neighbor classification and clustering. Min-max normalization performs a linear transformation on the original data. Suppose that min A and max A are the minimum and maximum values of an attribute, A, min-max normalization maps a value, vi, of A to vi-in the range [new minA,new maxA] by computing : v'= v%u2212minA (new_ maxA%u2212new_ minA) +new_ minA maxA%u2212minAExample. Let income range $12,000 to $98,000 normalized to [0.0, 1.0]. Then $73,000 is mapped to (1.0%u22120)+0 = 0.716Z-score normalization: v%u2212 Av'=AExample: Suppose that the mean and standard deviation of the values for the attribute income are $54,000 and $16,000, respectively. With zscore normalization, a value of $73,600 for income is transformed to =1.225Normalization by decimal scaling normalizes by moving the decimal point of values of attribute A. The number of decimal points moved depends on the maximum absolute value of A. A value, vi, of A is normalized to vi- by computing: vv'= j10Where j is the smallest integer such that Max(|%u03bd%u2019|) < 1