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336.#.#.a: Artículo

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351.#.#.b: Journal of Applied Research and Technology

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/1077/943

100.1.#.a: Agárdi, Anita; Kovács, László

524.#.#.a: Agárdi, Anita, et al. (2022). Clustering algorithms with prediction the optimal number of clusters. Journal of Applied Research and Technology; Vol. 20 Núm. 6, 2022; 638-651. Recuperado de https://repositorio.unam.mx/contenidos/4143023

245.1.0.a: Clustering algorithms with prediction the optimal number of clusters

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2022

264.#.1.c: 2022-12-23

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041.#.7.h: eng

520.3.#.a: The clustering is a widely used technique for grouping of objects. The objects, which are similar to each other should be in the same cluster. One disadvantage of general clustering algorithms is that the user must specify the number of clusters in advance, as input parameter. This is a major drawback since it is possible that the user cannot specify the number of clusters correctly, and the algorithm thus creates a clustering that puts very different elements into the same cluster. The aim of this paper is to present our representation and evaluation technique to determine the optimal cluster count automatically. With this technique, the algorithms itself determine the number of clusters. In this paper first, the classical clustering algorithms are introduced, then the construction and improvement algorithms and then our representation and evaluation method are presented. Then the performance of the algorithms with test results are compared.

773.1.#.t: Journal of Applied Research and Technology; Vol. 20 Núm. 6 (2022); 638-651

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

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300.#.#.a: Páginas: 638-651

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2022.20.6.1077

harvesting_date: 2023-11-08 13:10:00.0

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Artículo

Clustering algorithms with prediction the optimal number of clusters

Agárdi, Anita; Kovács, László

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

Agárdi, Anita, et al. (2022). Clustering algorithms with prediction the optimal number of clusters. Journal of Applied Research and Technology; Vol. 20 Núm. 6, 2022; 638-651. Recuperado de https://repositorio.unam.mx/contenidos/4143023

Descripción del recurso

Autor(es)
Agárdi, Anita; Kovács, László
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Clustering algorithms with prediction the optimal number of clusters
Fecha
2022-12-23
Resumen
The clustering is a widely used technique for grouping of objects. The objects, which are similar to each other should be in the same cluster. One disadvantage of general clustering algorithms is that the user must specify the number of clusters in advance, as input parameter. This is a major drawback since it is possible that the user cannot specify the number of clusters correctly, and the algorithm thus creates a clustering that puts very different elements into the same cluster. The aim of this paper is to present our representation and evaluation technique to determine the optimal cluster count automatically. With this technique, the algorithms itself determine the number of clusters. In this paper first, the classical clustering algorithms are introduced, then the construction and improvement algorithms and then our representation and evaluation method are presented. Then the performance of the algorithms with test results are compared.
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

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