Journal of Chemical Information & Computer Sciences
C4.5: programs for machine learning
C4.5: programs for machine learning
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
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Learning Logical Definitions from Relations
Machine Learning
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XRules: an effective structural classifier for XML data
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Kernels and Distances for Structured Data
Machine Learning
Data mining in bioinformatics using Weka
Bioinformatics
Discovery Science: 7th International Conference, DS 2004, Padova, Italy, October 2-5, 2004. Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Using classification to evaluate the output of confidence-based association rule mining
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Ensemble-Trees: Leveraging Ensemble Power Inside Decision Trees
DS '08 Proceedings of the 11th International Conference on Discovery Science
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster-grouping: from subgroup discovery to clustering
Machine Learning
Authorship classification: a syntactic tree mining approach
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Fast, effective molecular feature mining by local optimization
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Evaluating pattern set mining strategies in a constraint programming framework
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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We present Tree2, a new approach to structural classification. This integrated approach induces decision trees that test for pattern occurrence in the inner nodes. It combines state-of-the-art tree mining with sophisticated pruning techniques to find the most discriminative pattern in each node. In contrast to existing methods, Tree2 uses no heuristics and only a single, statistically well founded parameter has to be chosen by the user. The experiments show that Tree2 classifiers achieve good accuracies while the induced models are smaller than those of existing approaches, facilitating better comprehensibility.