Different types of data normalization methods are:
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Decimal Scaling:- This type of scaling transforms the data into a range between [-1,1]. The transformation formula is |
v'(i) = v(i)/10k
for the smallest k such that max( |v'(i)| ) ≤ 1.
e.g. - For the initial range [-991, 99], k is 3, and v = -991 becomes v' = -0.991.
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Min-Max Normalization:- This type of normalization transforms the the data into a desired range, usually [0,1]. The transformation formula is |
v’(i) = (v(i) - minA)/(maxA - minA)* (new_maxA - new_minA) + new_minA
where, [minA, maxA] is the initial range and [new_minA, new_maxA] is the new range.
e.g. - If v = 73600 in [12000, 98000] Þ v'= 0.716 in the new range [0,1].
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Zero-Mean normalization:- By using this type of normalization, the mean of the transformed set of data points is reduced to zero. For this, the mean and standard deviation of the initial set of data values are required. The transformation formula is |
v' = (v - meanA) / std_devA
where meanA and std_devA are the mean and standard deviation of the initial data values.
e.g. - If meanIncome = 54000, and std_devIncome = 16000, then v = 76000 Þ v'= 1.225.