Graph-Based Structural Pattern Learning

Abstract

The main objective of this project was to design, implement and evaluate new methods for performing pattern learning on structured data represented as graphs and evaluate their application to structural, relational databases relevant to the Evidence Assessment, Grouping, Linking and Evaluation (EAGLE) program. This work builds on existing methods for graph-based knowledge discovery and concept learning implemented in the SUBDUE structural pattern learning system. The graph-based structural pattern learning algorithm was extended to perform structural concept learning and structural, hierarchical conceptual clustering. The resulting system was evaluated using several structural databases, including those with known structural patterns, those of relevance to the target domains of the EAGLE program, and those developed as challenge problems within the EAGLE program.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2006
Accession Number
ADA456904

Entities

People

  • Diane Cook
  • Lawrence B. Holder

Organizations

  • University of Texas at Arlington

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Anomaly Detection
  • Artificial Intelligence
  • Change Detection
  • Clustering
  • Computer Programs
  • Counterterrorism
  • Data Mining
  • Database Management Systems
  • Databases
  • Detection
  • Information Science
  • Relational Database Management Systems
  • Relational Databases
  • Security
  • Structural Components

Readers

  • Computer Vision.
  • Graph Algorithms and Convex Optimization.
  • Systems Analysis and Design