A distributed shared nearest neighbors clustering algorithm

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Current data processing tasks require efficient approaches capable of dealing with large databases. A promising strategy consists in distributing the data along several computers that partially solves the undertaken problem. Then, these partial answers are integrated in order to obtain a final solution. We introduce the Distributed Shared Nearest Neighbor based clustering algorithm (D-SNN) which is able to work with disjoint partitions of data producing a global clustering solution that achieves a competitive performance regarding centralized approaches. Our algorithm is suited for large scale problems (e.g, text clustering) where data cannot be handled by a single machine due to memory size constraints. Experimental results over five data sets show that our proposal is competitive in terms of standard clustering quality performance measures.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
EditoresSergio Velastin, Marcelo Mendoza
EditorialSpringer Verlag
Páginas710-718
Número de páginas9
ISBN (versión impresa)9783319751924
DOI
EstadoPublicada - 2018
Publicado de forma externa
Evento22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017 - Valparaiso, Chile
Duración: 7 nov. 201710 nov. 2017

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10657 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017
País/TerritorioChile
CiudadValparaiso
Período7/11/1710/11/17

Huella

Profundice en los temas de investigación de 'A distributed shared nearest neighbors clustering algorithm'. En conjunto forman una huella única.

Citar esto