Why is scaling data important in data mining?

Scaling data is important in data mining primarily because it improves the performance of certain algorithms through normalization. Many algorithms, particularly those that use distances (like k-nearest neighbors, k-means clustering, and support vector machines), are sensitive to the scale of the data. This means that if features in the dataset have different scales, those with larger ranges can disproportionately influence the results, leading to biased outcomes.

Normalization and standardization techniques—such as min-max scaling or z-score normalization—adjust the data to a common scale. This results in a more balanced contribution from each feature, leading to better training of the model, improved convergence rates for optimization algorithms, and more reliable and interpretable results.

While scaling can affect the overall speed of data processing, its primary role is to ensure that algorithms operate effectively and produce accurate predictions, which is why the focus is on performance improvement rather than sheer speed or complexity increases.