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Data standardization (normalization) processing is a basic work of data mining. Different evaluation indicators often have different dimensions and dimensional units, which will affect the results of data analysis. In order to eliminate the dimensional influence between indicators, it is necessary to carry out data standardization processing to solve the comparability between data indicators. After the standardized processing of the original data, each index is in the same order of magnitude, which is suitable for comprehensive comparative evaluation. Here are two commonly used normalization methods:

I. min-max Standardization (Min-Max Normalization)

Also known as deviation normalization, it is a linear transformation of the original data so that the resulting value is mapped to [0-1]. The conversion function is as follows:

Max is the maximum value of sample data and min is the minimum value of sample data. A drawback of this approach is that when new data is added, it may lead to changes in max and min, which need to be redefined.

II. Z-score standardization method

This method standardizes the mean (mean) and standard deviation (standard deviation) of the original data. The processed data accords with the standard normal distribution, that is, the mean value is 0, the standard deviation is 1, and the conversion function is:

Clip_image006 is the mean value of all sample data, and clip_image008 is the standard deviation of all sample data.

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