C4.5: programs for machine learning
C4.5: programs for machine learning
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
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
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
TreeFinder: a First Step towards XML Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Mining of High Branching Factor Attribute Trees
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ACM SIGIR Forum
FAT-miner: mining frequent attribute trees
Proceedings of the 2007 ACM symposium on Applied computing
Subtree Testing and Closed Tree Mining Through Natural Representations
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Mining Frequent Closed Unordered Trees Through Natural Representations
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
Don't be afraid of simpler patterns
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
IMB3-Miner: mining induced/embedded subtrees by constraining the level of embedding
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Tree2: decision trees for tree structured data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Efficiently Mining Frequent Embedded Unordered Trees
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Constructing a Decision Tree for Graph-Structured Data and its Applications
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
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In recent years a variety of mining algorithms, to derive all frequent subtrees from a database of labeled ordered rooted trees has been developed. These algorithms share properties such as enumeration strategies and pruning techniques. They differ however in the tree inclusion relation used and the way attribute values are dealt with. In this work we investigate the different approaches with respect to 'usefulness' of the derived patterns, in particular, the performance of classifiers that use the derived patterns as features. In order to find a good trade-off between expressiveness and runtime performance of the different approaches, we also take the complexity of the different classifiers into account, as well as the run time and memory usage of the different approaches. The experiments are performed on two real data sets, and two synthetic data sets. The results show that significant improvement in both predictive performance and computational efficiency can be gained by choosing the right tree mining approach.