liigitamise

Liigitamine refers to the process of organizing items into categories or classes according to shared attributes or predefined rules. The goal is to enable efficient retrieval, comparison, and analysis by imposing an ordered structure on heterogeneous collections. The term is used across disciplines, including biology, information science, linguistics, and data management, and is closely related to classification, taxonomy, and categorization.

Methods of liigitamine range from manual schematization to automated techniques. In traditional contexts, experts define criteria and assign items to categories. In data science and machine learning, liigitamine often denotes supervised classification, where models learn from labeled examples to assign new instances to predefined labels. Unsupervised methods such as clustering group items by similarity when labels are not available. Classifications can be flat or hierarchical, and may rely on controlled vocabularies or ontologies to ensure consistency across domains.

Applications include library cataloging and taxonomy, biological taxonomy, information retrieval, and metadata management. In library science, classification systems organize books and resources for efficient access; in biology, taxonomy reflects evolutionary relationships and naming conventions; in information systems, liigitamine supports search, filtering, and data governance. Challenges include dealing with ambiguous items, evolving knowledge that shifts category definitions, and cross-domain boundaries where items may belong to multiple categories. Evaluation typically involves metrics such as accuracy, precision, recall, or domain-specific measures.