Qualitative Comparison of Graph-Based and Logic-Based Multi-Relational Data Mining: A Case Study

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 the 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.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2005
Accession Number
ADA459038

Entities

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  • Diane Cook
  • Lawrence B. Holder
  • Nikhil S. Ketkar

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  • University of Texas at Arlington

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