normázási

Normázási is a term used to describe the process of bringing data, measurements, or systems into conformity with a defined norm or standard. It is applied in several fields, each with its own methods and goals, but sharing the aim of improving comparability, consistency, and interpretability.

In statistics and data processing, normázás commonly means data normalization or standardization. This includes rescaling features to a common range (for example 0–1), adjusting to zero mean and unit variance (z-score standardization), or normalizing vectors by their length (L2 or L1 normalization). The purpose is to reduce the influence of scale on analyses and to improve the performance of machine learning algorithms or statistical tests.

In relational databases, normázás (more commonly referred to as database normalization) involves organizing data into separate tables to reduce redundancy and update anomalies. This process is guided by normal forms (1NF, 2NF, 3NF, BCNF, and beyond) and depends on functional dependencies. Benefits include data integrity and flexibility, while trade-offs may include increased schema complexity and potential performance considerations.

In image and signal processing, normázás refers to adjusting intensity or amplitude ranges to enable fair comparisons across samples or to prepare data for further processing. Techniques include min–max scaling, histogram normalization, and other adaptive schemes that influence contrast and perceptual uniformity.

In metrology and measurement, normázás covers calibration and conversion of measurements to reference standards, ensuring comparability across instruments and laboratories.

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