Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '01 Proceedings of the 2001 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
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Novel Efficient Algorithm for Determining Maximum Common Subgraphs
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
Efficient Mining of Contrast Patterns and Their Applications to Classification
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
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
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient mining of minimal distinguishing subgraph patterns from graph databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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This paper introduces a new type of discriminative subgraph pattern called breaker emerging subgraph pattern by introducing three constraints and two new concepts: base and breaker. A breaker emerging subgraph pattern consists of three subpatterns: a constrained emerging subgraph pattern, a set of bases and a set of breakers. An efficient approach is proposed for the discovery of top-k breaker emerging subgraph patterns from graph datasets. Experimental results show that the approach is capable of efficiently discovering top-k breaker emerging subgraph patterns from given datasets, is more efficient than two previous methods for mining discriminative subgraph patterns. The discovered top-k breaker emerging subgraph patterns are more informative, more discriminative, more accurate and more compact than the minimal distinguishing subgraph patterns. The top-k breaker emerging patterns are more useful for substructure analysis, such as molecular fragment analysis.