Normaliseerimismeetodi

Normaliseerimismeetodi refers to a method used in various fields, particularly in statistics and data analysis, to bring different datasets or variables to a common scale. This process is essential when working with data that has different units, ranges, or distributions. By normalising data, researchers can ensure that no single variable unduly influences the results due to its scale.

The core idea behind normalisation is to transform data so that it fits within a specific range, commonly between 0 and 1, or to have a mean of 0 and a standard deviation of 1 (this latter process is often referred to as standardisation). Several techniques can be employed for normalisation, including min-max scaling, z-score normalisation, and decimal scaling.

Min-max scaling linearly transforms the data to fit within a specified range. Z-score normalisation, on the other hand, centers the data around zero and scales it by its standard deviation, making it useful for outlier detection. Decimal scaling involves moving the decimal point in the values to bring them within a specific range.

The choice of normalisation method depends on the specific application and the characteristics of the data. Normalisation is a crucial preprocessing step in many machine learning algorithms, such as support vector machines, k-nearest neighbours, and neural networks, as it can significantly improve their performance and convergence speed. It is also used in image processing, finance, and other areas where comparing data with differing scales is necessary.