A vector space model for automatic indexing

Datos Bibliográficos

ID23323563
AutoresGerard Salton (Cornell University), Anita M-y Wong (0000-0003-0783-5089, Cornell University), A Wong (0009-0007-4700-6740, Cornell Univ., Ithaca, NY), C S Yang (Cornell Univ., Ithaca, NY), Chul‐Su Yang (0000-0003-4918-961X, Cornell University)
Año1975
Volumen18
Número11
Páginas613-620
Fecha de publicación1975-11-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaCommunications of the ACM (JOURNAL)
Identificadores de la revistaISSN: 0001-0782 • E-ISSN: 1557-7317
EditorialAssociation for Computing Machinery (ACM) (PUBLISHER)
DOI10.1145/361219.361220
OpenAlexW2165612380
IdiomaEN
Citas recibidas90
Referencias citadas2

In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model.

Algorithm · Computation · Data mining · Function (biology) · Information retrieval · Matching (statistics) · Property (philosophy) · Search engine indexing · Space (punctuation) · Vector space model · Computer Science · Data Management and Algorithms · Data Mining Algorithms and Applications · Image Retrieval and Classification Techniques · Mathematics