ML92 Proceedings of the ninth international workshop on Machine learning
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
RAM: Randomized Approximate Graph Mining
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
gApprox: Mining Frequent Approximate Patterns from a Massive Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Energy-Based Perceptual Segmentation Using an Irregular Pyramid
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Frequent subgraph pattern mining on uncertain graph data
Proceedings of the 18th ACM conference on Information and knowledge management
Text classification using graph mining-based feature extraction
Knowledge-Based Systems
Corpus callosum MR image classification
Knowledge-Based Systems
Duplicate candidate elimination and fast support calculation for frequent subgraph mining
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Efficient algorithms for node disjoint subgraph homeomorphism determination
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
A new algorithm for mining frequent connected subgraphs based on adjacency matrices
Intelligent Data Analysis
Mining Frequent Subgraph Patterns from Uncertain Graph Data
IEEE Transactions on Knowledge and Data Engineering
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
Frequent sub-graph mining on edge weighted graphs
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Region of interest based image categorization
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Frequent tree pattern mining: A survey
Intelligent Data Analysis
Image Classification Using Subgraph Histogram Representation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Assessing the role of spatial relations for the object recognition task
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Mining fuzzy specific rare itemsets for education data
Knowledge-Based Systems
An efficient graph-mining method for complicated and noisy data with real-world applications
Knowledge and Information Systems - Special Issue on "Context-Aware Data Mining (CADM)"
A new proposal for graph classification using frequent geometric subgraphs
Data & Knowledge Engineering
Graph-based approach for human action recognition using spatio-temporal features
Journal of Visual Communication and Image Representation
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The use of approximate graph matching for frequent subgraph mining has been identified in different applications as a need. To meet this need, several algorithms have been developed, but there are applications where it has not been used yet, for example image classification. In this paper, a new algorithm for mining frequent connected subgraphs over undirected and labeled graph collections VEAM (Vertex and Edge Approximate graph Miner) is presented. Slight variations of the data, keeping the topology of the graphs, are allowed in this algorithm. Approximate matching in existing algorithm (APGM) is only performed on vertex label set. In VEAM, the approximate matching between edge label set in frequent subgraph mining is included in the mining process. Also, a framework for graph-based image classification is introduced. The approximate method of VEAM was tested on an artificial image collection using a graph-based image representation proposed in this paper. The experimentation on this collection shows that our proposal gets better results than graph-based image classification using some algorithms reported in related work.