Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Share Based Measures for Itemsets
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
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
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
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Text classification using graph mining-based feature extraction
Knowledge-Based Systems
Corpus callosum MR image classification
Knowledge-Based Systems
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Detecting anomalies in graphs with numeric labels
Proceedings of the 20th ACM international conference on Information and knowledge management
Frequent approximate subgraphs as features for graph-based image classification
Knowledge-Based Systems
Finding the most descriptive substructures in graphs with discrete and numeric labels
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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Frequent sub-graph mining entails two significant overheads. The first is concerned with candidate set generation. The second with isomorphism checking. These are also issues with respect to other forms of frequent pattern mining but are exacerbated in the context of frequent sub-graph mining. To reduced the search space, and address these twin overheads, a weighted approach to sub-graph mining is proposed. However, a significant issue in weighted sub-graph mining is that the antimonotone property, typically used to control candidate set generation, no longer holds. This paper examines a number of edge weighting schemes; and suggests three strategies for controlling candidate set generation. The three strategies have been incorporated into weighted variations of gSpan: ATW-gSpan, AW-gSpan and UBW-gSpan respectively. A complete evaluation of all three approaches is presented.