Enhancement of performance of document clustering in the authorship identification problem with a weighted cosine similarity

Carolina Martín-del-Campo-Rodríguez, Grigori Sidorov, Ildar Batyrshin

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

Distance and similarity measures are essential to solve many pattern recognition problems such as classification, information retrieval and clustering, where the use of a specific distance could led to a better performance than others. A weighted cosine distance is proposed considering a variation in the weights of exclusive attributes of the input vectors. An agglomerative hierarchical clustering of documents was used for the comparison between the traditional cosine similarity and the one proposed in this paper. This modified measure has outcome in an improvement in the formation of clusters.

Idioma originalInglés
Título de la publicación alojadaAdvances in Computational Intelligence - 17th Mexican International Conference on Artificial Intelligence, MICAI 2018, Proceedings
EditoresIldar Batyrshin, María de Lourdes Martínez-Villaseñor, Hiram Eredín Ponce Espinosa
EditorialSpringer Verlag
Páginas49-56
Número de páginas8
ISBN (versión impresa)9783030044961
DOI
EstadoPublicada - 2018
Evento17th Mexican International Conference on Artificial Intelligence, MICAI 2018 - Guadalajara, México
Duración: 22 oct. 201827 oct. 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11289 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia17th Mexican International Conference on Artificial Intelligence, MICAI 2018
País/TerritorioMéxico
CiudadGuadalajara
Período22/10/1827/10/18

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