Please use this identifier to cite or link to this item: http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1395
A new algorithm for mining frequent connected subgraphs based on adjacency matrices
ANDRÉS GAGO ALONSO
Abel Puentes Luberta
JESUS ARIEL CARRASCO OCHOA
JOSE FRANCISCO MARTINEZ TRINIDAD
Acceso Abierto
Atribución-NoComercial-SinDerivadas
Data mining
Graph mining
Frequent subgraphs
Labeled graphs
Canonical adjacency matrices
Most of the Frequent Connected Subgraph Mining (FCSM) algorithms have been focused on detecting duplicate candidates using canonical form (CF) tests. CF tests have high computational complexity, which affects the efficiency of graph miners. In this paper, we introduce novel properties of the canonical adjacency matrices for reducing the number of CF tests in FCSM. Based on these properties, a new algorithm for frequent connected subgraph mining called grCAM is proposed. The experiments on real world datasets show the impact of the proposed properties in FCSM. Besides, the performance of our algorithm is compared against some other reported algorithms.
IOS Press
2010
Artículo
Inglés
Estudiantes
Investigadores
Público en general
Gago-Alonso, A., et al., (2010). A new algorithm for mining frequent connected subgraphs based on adjacency matrices, Intelligent Data Analysis, (May): 1-26
CIENCIA DE LOS ORDENADORES
Versión aceptada
acceptedVersion - Versión aceptada
Appears in Collections:Artículos de Ciencias Computacionales

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