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dc.contributor.author
Dos Santos, Daniel Andrés
dc.contributor.author
Deutsch, Reena
dc.date.available
2019-04-30T15:00:59Z
dc.date.issued
2010-09
dc.identifier.citation
Dos Santos, Daniel Andrés; Deutsch, Reena; The Positive Matching Index: A new similarity measure with optimal characteristics; Elsevier Science; Pattern Recognition Letters; 31; 12; 9-2010; 1570-1576
dc.identifier.issn
0167-8655
dc.identifier.uri
http://hdl.handle.net/11336/75324
dc.description.abstract
Despite the many coefficients accounting for the resemblance between pairs of objects based on presence/absence data, no one measure shows optimal characteristics. In this work the Positive Matching Index (PMI) is proposed as a new measure of similarity between lists of attributes. PMI fulfills the Tulloss' theoretical prerequisites for similarity coefficients, is easy to calculate and has an intrinsic meaning expressable into a natural language. PMI is bounded between 0 and 1 and represents the mean proportion of positive matches relative to the size of attribute lists, ranging this cardinality continuously from the smaller list to the larger one. PMI behaves correctly where alternative indices either fail, or only approximate to the desirable properties for a similarity index. Empirical examples associated to biomedical research are provided to show outperformance of PMI in relation to standard indices such as Jaccard and Dice coefficients.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Association Coefficient
dc.subject
Binary Data
dc.subject
Dice Index
dc.subject
Jaccard Index
dc.subject
Similarity
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
The Positive Matching Index: A new similarity measure with optimal characteristics
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-04-23T17:50:31Z
dc.journal.volume
31
dc.journal.number
12
dc.journal.pagination
1570-1576
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Dos Santos, Daniel Andrés. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina
dc.description.fil
Fil: Deutsch, Reena. University of California at San Diego; Estados Unidos
dc.journal.title
Pattern Recognition Letters
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167865510000917
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1016/j.patrec.2010.03.010
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