Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pushing Support Constraints Into Association Rules Mining
IEEE Transactions on Knowledge and Data Engineering
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 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
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In general, patterns that contain only a few subgraphs will tend to be interesting if they have a high support, whereas larger patterns can still be interesting even if their support is relatively small. A better solution lies in exploiting support constraints, which specify how weakening support is required for what subgraphs, so that only the necessary subgraphs are generated. In this paper, a framework of frequent partially labeled subgraphs mining is presented in the presence of support constraints, which is based on pattern weakening. This approach is to push forward support constraints into the process of mining so that the proper support is determined for larger subgraphs at runtime to preserve the essence of mining result. The performance of algorithm is evaluated in a multi-group synthetic datasets, and the experimental results show that the method is efficient and fast.