C4.5: programs for machine learning
C4.5: programs for machine learning
A database perspective on knowledge discovery
Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
Computational learning theory and natural learning systems: Volume IV
Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Optimal conversion of extended-entry decision tables with general cost criteria
Communications of the ACM
The synthetic approach to decision table conversion
Communications of the ACM
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Building Decision Trees with Constraints
Data Mining and Knowledge Discovery
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
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
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Inference for the Generalization Error
Machine Learning
DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints
Data Mining and Knowledge Discovery
Memory issues in frequent itemset mining
Proceedings of the 2004 ACM symposium on Applied computing
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Maximally informative k-itemsets and their efficient discovery
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Extending the state-of-the-art of constraint-based pattern discovery
Data & Knowledge Engineering
Machine Learning
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
IEEE Transactions on Computers
An Algorithm for Constructing Optimal Binary Decision Trees
IEEE Transactions on Computers
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Exploiting informative priors for Bayesian classification and regression trees
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
k-Anonymous Decision Tree Induction
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Guest Editorial: Global modeling using local patterns
Data Mining and Knowledge Discovery
Itemset mining: A constraint programming perspective
Artificial Intelligence
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In this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard problem of finding optimal decision trees in a wide range of applications by post-processing a set of patterns: we use local patterns to construct a global model. We exploit the connection between constraints in pattern mining and constraints in decision tree induction to develop a framework for categorizing decision tree mining constraints. This framework allows us to determine which model constraints can be pushed deeply into the pattern mining process, and allows us to improve the state-of-the-art of optimal decision tree induction.