Why is data normalization important in data mining?

Data normalization is crucial in data mining because it ensures uniformity in data scales, which allows for effective comparison and analysis. When datasets originate from various sources or contain features with different units of measurement, their scales can vastly differ. For example, one feature might be measured in thousands while another in single digits. Without normalization, algorithms may place undue weight on features with larger numerical ranges, leading to biased results.

When data is normalized, each feature is transformed to a similar scale, thereby facilitating a more accurate assessment of their relationships and contributions to the model. This is particularly important for algorithms sensitive to the magnitude of data, such as k-nearest neighbors or support vector machines. Normalization helps in providing more meaningful results by ensuring that each variable contributes equally to the distance calculations.

Thus, the essence of normalization lies in creating a consistent framework for measurements, improving the quality of analysis and insights derived from the data.