Definition of «normalization»

Usage examples

1. Data normalization: In data science, normalization refers to the process of standardizing and transforming numerical data to a common scale. For example, scaling features like height, weight, and age to a specific range like 0-1 to facilitate accurate comparison between records.
2. Linguistic normalization: In natural language processing, normalization involves transforming and standardizing text data by removing punctuation, converting all characters to lowercase, expanding abbreviations, and correcting spelling errors. This ensures consistency and improves the accuracy of text analysis and language modeling tasks.
3. Database normalization: In database design, normalization is used to eliminate redundancy and improve data integrity by breaking large tables into smaller ones and establishing relationships between them. Normalization reduces data duplication, improves query performance, and enhances data management and maintenance.
4. Audio normalization: In audio engineering, normalization is the process of adjusting the volume of an audio signal to a standard or desired level. It ensures that the audio remains clear and consistent throughout, avoiding abrupt changes in volume that can affect the listening experience. Normalization is often applied during audio post-production or mastering stages.
5. Image normalization: In image processing, normalization involves adjusting the brightness, contrast, and color levels of an image to achieve a standardized appearance. It is used to enhance visibility, correct color imbalance, and remove unwanted variations caused by factors like lighting conditions or image acquisition devices. Normalization techniques include histogram equalization, adaptive contrast enhancement, and color correction algorithms.