Data mining and knowledge discovery in databases
Communications of the ACM
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Top-down induction of first-order logical decision trees
Artificial Intelligence
Coca: an automated debugger for C
Proceedings of the 21st international conference on Software engineering
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Generic program monitoring by trace analysis
Theory and Practice of Logic Programming
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
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We present a trace based approach for analyzing the runs of Inductive Logic Programming Data Mining systems, without needing to modify the actual implementation of the ILP mining algorithms. We discuss the use of traces as the basis for easy and fast, semi-automated debugging of the underlying (query) execution engine of the ILP system. Our approach also provides a way to monitor the behavior of queries generated by the ILP algorithm, allowing an evaluation and comparison of the impact of different execution mechanisms. The traces can be extended further, and as such be useful for visualization and monitoring of other aspects of ILP data mining systems.