Journal article
Comparison of graph-based and logic-based multi-relational data mining
SIGKDD explorations, Vol.7(2), pp.64-71
12/2005
Handle:
https://hdl.handle.net/2376/113838
Abstract
We perform an experimental comparison of the graph-based multi-relational data mining system, Subdue, and the inductive logic programming system, CProgol, on the Mutagenesis dataset and various artificially generated Bongard problems. Experimental results indicate that Subdue can significantly outperform CProgol while discovering structurally large multi-relational concepts. It is also observed that CProgol is better at learning semantically complicated concepts and it tends to use background knowledge more effectively than Subdue. An analysis of the results indicates that the differences in the performance of the systems are a result of the difference in the expressiveness of the logic-based and the graph-based representations. The ability of graph-based systems to learn structurally large concepts comes from the use of a weaker representation whose expressiveness is intermediate between propositional and first-order logic. The use of this weaker representation is advantageous while learning structurally large concepts but it limits the learning of semantically complicated concepts and the utilization background knowledge.
Metrics
16 Record Views
Details
- Title
- Comparison of graph-based and logic-based multi-relational data mining
- Creators
- Nikhil S Ketkar - University of Texas at ArlingtonLawrence B Holder - University of Texas at ArlingtonDiane J Cook - University of Texas at Arlington
- Publication Details
- SIGKDD explorations, Vol.7(2), pp.64-71
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Identifiers
- 99900548374101842
- Language
- English
- Resource Type
- Journal article