Normalointi

Normalointi (normalization) is the process of transforming data or signals to a common scale or standard. It is used to make different measures comparable, to improve numerical stability, and to prepare data for further analysis. The concept appears in statistics, data science, database design, natural language processing, and signal processing.

In statistics and machine learning, common forms include min‑max normalization, which rescales features to a fixed range (often 0 to 1), and z-score standardization, which centers data at zero with unit variance. Vector normalization divides a vector by its length to produce a unit vector. Normalization can reduce the influence of differing scales among features and can affect the behavior of learning algorithms and distance measures.

Database normalization is a design approach to reduce data redundancy and improve data integrity. It uses normal forms (1NF, 2NF, 3NF, BCNF) to structure tables so that non-key attributes depend only on primary keys. This often entails splitting large tables into smaller, related ones and defining clear functional dependencies. While normalization promotes data consistency, denormalization is sometimes used to optimize read performance.

In text processing, normalization standardizes text for analysis, including case folding, character normalization, and removing diacritics. In signal processing, amplitude normalization adjusts the peak or RMS level of audio or other signals to a reference value.

Choosing a normalization method depends on the data and task. Improper normalization can distort distributions or relationships, introduce leakage in evaluation, or affect interpretability. Careful consideration of assumptions and downstream effects is required.