Conference proceeding
Qualitative comparison of graph-based and logic-based multi-relational data mining: a case study
Proceedings of the 4th international workshop on multi-relational mining, pp.25-32
MRDM '05
08/21/2005
Handle:
https://hdl.handle.net/2376/113544
Abstract
The goal of this paper is to generate insights about the differences between graph-based and logic-based approaches to multi-relational data mining by performing a case study of graph-based system, Subdue and the inductive logic programming system, CProgol. We identify three key factors for comparing graph-based and logic-based multi-relational data mining; namely, the ability to discover structurally large concepts, the ability to discover semantically complicated concepts and the ability to effectively utilize background knowledge. We perform an experimental comparison of Subdue and CProgol on the Mutagenesis domain 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.
Metrics
14 Record Views
Details
- Title
- Qualitative comparison of graph-based and logic-based multi-relational data mining
- Creators
- Nikhil KetkarLawrence HolderDiane Cook
- Publication Details
- Proceedings of the 4th international workshop on multi-relational mining, pp.25-32
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Series
- MRDM '05
- Publisher
- ACM
- Identifiers
- 99900548086201842
- Language
- English
- Resource Type
- Conference proceeding