Graph-Based Analysis of Nuclear Smuggling Data DIANE COOK and LAWRENCE HOLDER

Graph-based Relational Learning with Application to Security

We describe an approach to learning patterns in relational data represented as a graph. The approach, implemented in the Subdue system, searches for patterns that maximally compress the input graph. Subdue can be used for supervised learning, as well as unsupervised pattern discovery and clustering. We apply Subdue in domains related to homeland security and social network analysis.

Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming Systems

Title: Graph-based Hierarchical Conceptual Clustering Graph-based Hierarchical Conceptual Clustering

Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graphbased representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we present Subdue and focus on its clustering capabilities. We use two examples to illustrate the val...

Fuzzy Substructure Discovery

This paper describes a method for discovering substructures in data using a fuzzy graph match. A previous implementation of the Subdue system discovers substructures based on the psychologically-motivated criteria of cognitive savings, compactness, connec-tivity and coverage. However, the instances in the data must exactly match the discovered substructures. We describe a new implementation of ...

Graph-Based Hierarchical Conceptual Clustering

Hierarchical conceptual clustering has proven to be a useful, although under-explored, data mining technique. A graph-based representation of structural information combined with a substructure discovery technique has been shown to be successful in knowledge discovery. The SUBDUE substructure discovery system provides one such combination of approaches. This work presents SUBDUE and the develop...