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An Information Measure for Classification
Open accessAbstract
This paper derives a measure of the goodness of a classification based on information theory. A classification is regarded as a method of economical statistical encoding of the available attribute information. The measure may be used to compare the relative goodness of classifications produced by different methods or as the basis of a classification procedure. A classification program, ‘SNOB’, has been written for the University of Sydney KDF 9 computer, and first tests show good agreement with conventional taxonomy.
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Metadata
- Authors
- Wallace, C. S., & Boulton, D. M.
- Venue
- The Computer Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 1968-08-01
- Volume
- 11
- Issue
- 2
- Pages
- 185-194
- ISSN
- 0010-4620 (ISSN-L)
- 1460-2067
- OpenAlex
- W2110381504
- Cited by
- 933
- References
- 1
- FWCI
- 22.32
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
