A new algorithm for mining frequent connected subgraphs based on adjacency matrices

Authors:
Andrés Gago-Alonso;Abel Puentes-Luberta;Jesús A. Carrasco-Ochoa;José E. Medina-Pagola;José Fco. Martínez-Trinidad
Affiliations:
(Correspd. E-mail: agago@cenatav.co.cu) Advanced Technologies Application Center, Havana, Cuba and Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, ...;Advanced Technologies Application Center, Havana, Cuba and Faculty of Mathematics and Computer Sciences, University of Havana, Havana, Cuba;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, México;Advanced Technologies Application Center, Havana, Cuba;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, México
Venue:
Intelligent Data Analysis
Year:
2010

Citing 13
Cited 1

Frequent Subgraph Discovery

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Molecular Fragments: Finding Relevant Substructures of Molecules

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A quickstart in frequent structure mining can make a difference

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent pattern mining: current status and future directions

Data Mining and Knowledge Discovery
GDClust: A Graph-Based Document Clustering Technique

ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Direct mining of discriminative and essential frequent patterns via model-based search tree

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Connected Subgraphs Reducing the Number of Candidates

ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
The predictive toxicology evaluation challenge

IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
A quantitative comparison of the subgraph miners mofa, gspan, FFSM, and gaston

PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases

Frequent approximate subgraphs as features for graph-based image classification

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.