normaleisering

Normaleisering is a general term for transforming data, signals or information so that they can be compared, stored or processed against a common standard. The term is used in several disciplines, sometimes with discipline-specific methods and goals.

In statistics and data science, normalization refers to rescaling data to a standard range or distribution. Common techniques include min–max scaling, which maps values to a fixed interval (often 0 to 1), and z-score standardization, which centers data on zero with unit variance. Normalization can improve numerical stability, facilitate convergence for machine learning algorithms, and ensure that features contribute equally. It may alter interpretability, and the choice of method depends on the data and task.

In database design, normalization is the process of structuring a database to reduce redundancy and update anomalies. It typically involves decomposing tables into smaller, related relations and applying normal forms (1NF, 2NF, 3NF, BCNF, etc.). The goals are data integrity and maintainability, but normalization can complicate queries and affect performance.

In text and data processing, normalization includes converting text to a canonical form, such as lowercasing, removing diacritics, Unicode normalization (NFC/NFD), and tokenization rules. In audio and image processing, normalization adjusts the signal level or pixel values to a target range to ensure consistent perceived loudness or brightness.

Normaleisering also intersects with policy and measurement standards, for example to compare results across studies or systems. The choice of normalization strategy should be documented to support reproducibility.

See also: normalization, standardization, data preprocessing, database normalization.