Título

An algorithm based on density and compactness for dynamic 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) Clustering - (CLUSTERING) Overlapping clustering algorithms - (OVERLAPPING CLUSTERING ALGORITHMS) Dynamic clustering algorithms - (DYNAMIC CLUSTERING 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

Most clustering algorithms organize a collection of objects into a set of disjoint clusters. Although this approach has been successfully applied in unsupervised learning, there are several applications where objects could belong to more than one cluster. Overlapping clustering is an alternative in those contexts like social network analysis, information retrieval and bioinformatics, among other problems where non-disjoint clusters appear. In addition, there are environments where the collection changes frequently and the clustering must be updated; however, most of the existing overlapping clustering algorithms are not able to efficiently update the clustering. In this paper, we introduce a new overlapping clustering algorithm, called DClustR, which is based on the graph theory approach and it introduces a new strategy for building more accurate overlapping clusters than those built by state-of-the-art algorithms. Moreover, our algorithm introduces a new strategy for efficiently updating the clustering when the collection changes. The experimentation conducted over several standard collections shows the good performance of the proposed algorithm, wrt. accuracy and efficiency.

Editor

Elsevier Ltd.

Fecha de publicación

2013

Tipo de publicación

Artículo

Versión de la publicación

Versión aceptada

Recurso de información

http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2390

Formato

application/pdf

Idioma

Inglés

Audiencia

Estudiantes

Investigadores

Público en general

Sugerencia de citación

Pérez, a., et al., (2013). An algorithm based on density and compactness for dynamic overlapping clustering, Pattern Recognition, Vol. 2013 (46): 3040-3055

Repositorio Orígen

Repositorio Institucional del INAOE

Descargas

106