In the context of data mining, what is normalization?

Normalization in the context of data mining refers to the adjustment of values into a common scale. This is an essential step in preprocessing data before applying algorithms that are sensitive to the scale or distribution of the input features.

When data is collected from various sources, it often comes in different units or ranges, which can lead to issues when applying machine learning models. For instance, if one feature is measured in hundreds and another in thousands, the model may become biased toward the feature with the larger scale. By normalizing the data, each feature is transformed to a common scale, typically ranging from 0 to 1 or –1 to 1. This process helps ensure that the influence of each feature on the model is balanced, enabling the algorithms to learn more effectively.

Normalization techniques include min-max scaling, z-score standardization, and others, depending on the requirements of the specific data mining task being performed. This way, normalization directly supports the goal of improving model performance and accuracy.