C4.5: programs for machine learning
C4.5: programs for machine learning
A database perspective on knowledge discovery
Communications of the ACM
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Building knowledge scouts using KGL metalanguage
Fundamenta Informaticae
Machine Learning
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Building Decision Trees with Constraints
Data Mining and Knowledge Discovery
Incremental Induction of Decision Trees
Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Knowledge Representation and Inductive Learning with XML
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Journal of Artificial Intelligence Research
Integrating pattern mining in relational databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Inductive databases in the relational model: the data as the bridge
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Constraint based induction of multi-objective regression trees
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Mining optimal decision trees from itemset lattices
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An inductive database prototype based on virtual mining views
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Knowledge Representation in Difficult Medical Diagnosis
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Software—Practice & Experience
Analysis of time series data with predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
An inductive database system based on virtual mining views
Data Mining and Knowledge Discovery
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In inductive databases, there is no conceptual difference between data and the models describing the data: both can be stored and queried using some query language. The approach that adheres most strictly to this philosophy is probably the one proposed by Calders et al. (2006): in this approach, models are stored in relational tables and queried using standard SQL. The approach has been described in detail for association rule discovery. In this work, we study how decision tree induction can be integrated in this approach. We propose a representation format for decision trees similar to the format proposed earlier for association rules, and queryable using standard SQL; and we present a prototype system in which part of the needed functionality is implemented. In particular, we have developed an exhaustive tree learning algorithm able to answer a wide range of constrained queries.