Título

OClustR: A new graph-based algorithm for overlapping clustering

Autor

AIREL PEREZ SUAREZ

José Francisco Martínez Trinidad

Jesús Ariel Carrasco Ochoa

José Eladio Medina Pagola

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc-nd/4.0

Materias

Data mining - (DATA MINING) Overlapping clustering - (OVERLAPPING CLUSTERING) Graph-based algorithms - (GRAPH-BASED ALGORITHMS) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)

Resumen o descripción

Clustering is a Data Mining technique, which has been widely used in many practical applications. From these applications, there are some, like social network analysis, topic detection and tracking, information retrieval, categorization of digital libraries, among others, where objects may belong to more than one cluster; however, most clustering algorithms build disjoint clusters. In this work, we introduce OClustR, a new graph-based clustering algorithm for building overlapping clusters. The proposed algorithm introduces a new graph-covering strategy and a new filtering strategy, which together allow to build overlapping clusterings more accurately than those built by previous algorithms. The experimental evaluation, conducted over several standard collections, showed that our proposed algorithm builds less clusters than those built by the previous related algorithms. Additionally, OClustR builds clusters with overlapping closer to the real overlapping in the collections than the overlapping generated by other clustering algorithms.

Editor

Elsevier B.V.

Fecha de publicación

2013

Tipo de publicación

Artículo

Versión de la publicación

Versión aceptada

Recurso de información

http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2388

Formato

application/pdf

Idioma

Inglés

Audiencia

Estudiantes

Maestros

Público en general

Sugerencia de citación

Pérez, A., et al., (2013). OClustR: A new graph-based algorithm for overlapping clustering, Neurocomputing Vol. 2013 (121): 234-247

Repositorio Orígen

Repositorio Institucional del INAOE

Descargas

297