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100.1.#.a: Angeles, Maria Del Pilar; Monreal, Carlos G. Ortiz

524.#.#.a: Angeles, Maria Del Pilar, et al. (2019). An attribute-based classification by threshold to enhance the data matching process. Journal of Applied Research and Technology; Vol 17 No 4, 2019. Recuperado de https://repositorio.unam.mx/contenidos/4110233

245.1.0.a: An attribute-based classification by threshold to enhance the data matching process

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264.#.0.c: 2019

264.#.1.c: 2019-11-05

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520.3.#.a: The problem of detection and classification of extensional inconsistencies during data integration of disparate data sources affects business competitiveness. A number of classification methods have been utilized until now, but there still some work to do in terms of effectiveness and performance. The paper shows the proposal, implementation, and evaluation of a new classification algorithm called Attribute-based Classification by Threshold that overcomes the disadvantages of the Threshold-based Classification. We have carried aout an evaluation of quality of the data matching process by comparing Threshold-based Classification, Farthest First and K-means against the proposed algorithm. The Attribute-based Classification by Threshold has a better performance than the rest of the unsupervised classification methods.

773.1.#.t: Journal of Applied Research and Technology; Vol 17 No 4

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046.#.#.j: 2021-04-13 00:00:00.000000

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doi: https://doi.org/10.22201/icat.16656423.2019.17.4.861

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Artículo

An attribute-based classification by threshold to enhance the data matching process

Angeles, Maria Del Pilar; Monreal, Carlos G. Ortiz

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Angeles, Maria Del Pilar, et al. (2019). An attribute-based classification by threshold to enhance the data matching process. Journal of Applied Research and Technology; Vol 17 No 4, 2019. Recuperado de https://repositorio.unam.mx/contenidos/4110233

Descripción del recurso

Autor(es)
Angeles, Maria Del Pilar; Monreal, Carlos G. Ortiz
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
An attribute-based classification by threshold to enhance the data matching process
Fecha
2019-11-05
Resumen
The problem of detection and classification of extensional inconsistencies during data integration of disparate data sources affects business competitiveness. A number of classification methods have been utilized until now, but there still some work to do in terms of effectiveness and performance. The paper shows the proposal, implementation, and evaluation of a new classification algorithm called Attribute-based Classification by Threshold that overcomes the disadvantages of the Threshold-based Classification. We have carried aout an evaluation of quality of the data matching process by comparing Threshold-based Classification, Farthest First and K-means against the proposed algorithm. The Attribute-based Classification by Threshold has a better performance than the rest of the unsupervised classification methods.
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces