normalizing

Normalizing refers to the process of bringing something into conformity with a standard or norm, or making data or processes consistent with a reference.

In statistics and data science, normalization describes transforming data to a common scale, such as min-max scaling to a [0,1] interval or standardization to zero mean and unit variance. The purpose is to improve comparability, numerical stability, and the performance of various algorithms.

In relational databases, normalization is a design method to organize data to reduce redundancy and improve data integrity. It uses a sequence of normal forms (1NF, 2NF, 3NF, BCNF). The process involves decomposing tables and enforcing functional dependencies to minimize anomalies.

In signal processing and audio, normalization adjusts the amplitude of a signal to a target level, often maximizing the peak or average level without clipping. It helps to standardize loudness across recordings or tracks.

In text processing, normalization converts text into a canonical form, including case folding, accent removal, Unicode normalization, and standardizing spellings to improve search, indexing, or comparison across documents.

In other contexts, normalization can refer to standardizing laboratory measurements, normalizing gene expression data, or the sociological concept of making deviant behavior appear normal through gradual social acceptance. Across disciplines, it denotes converting to a common standard to facilitate comparison, interoperability, or integrity.