Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Graph-Based Relational Concept Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Discovery of Surprising Exception Rules Based on Intensity of Implication
PKDD '98 Proceedings of the Second 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
Unified Algorithm for Undirected Discovery of Execption Rules
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Answering the Most Correlated N Association Rules Efficiently
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The levelwise version space algorithm and its application to molecular fragment finding
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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In recent years, the problem of mining association rulesover frequent itemsets in transactional data has been frequentlystudied and yielded several algorithms that can findassociation rules within a limited amount of time. Alsomore complex patterns have been considered such as orderedtrees, unordered trees, or labeled graphs. Althoughsome approaches can efficiently derive all frequent subgraphsfrom a massive dataset of graphs, a subgraph orsubtree that is mathematically defined is not necessarily abetter knowledge representation. In this paper, we proposean efficient approach to discover significant rules to classifypositive and negative graph examples by estimating atight upper bound on the statistical metric. This approachabandons unimportant rules earlier in the computations,and thereby accelerates the overall performance. The performancehas been evaluated using real world datasets, andthe efficiency and effect of our approach has been confirmedwith respect to the amount of data and the computation time.