Knowledge Quality Functions for Rule Discovery

Abstract

The Department of Defense (DoD) possesses tremendous amounts of data stored in many large databases. Due to the size of these databases, humans are incapable of efficiently discovering interesting and useful patterns so an automated data-mining tool is necessary. Output in the form of production rules, ie., 'If y Then x,' is preferred because they are understandable by humans and support decision making processes. This thesis investigates the manner in which data-mining systems discover useful, interesting, but currently unavailable knowledge. The search and evaluation process, guided by a knowledge quality function, is the key task of a data-mining system. This thesis evaluates three knowledge quality functions taken from the literature. Each knowledge quality function discovers new and interesting sets of rules reflecting different characteristics of knowledge. DoD applications are suggested for each of the knowledge quality functions. Knowledge discovery, Data-mining, Fitness, Quality functions.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1994
Accession Number
ADA285513

Entities

People

  • Elizabeth S. Walters
  • Frank J. Bunn

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Cells
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Analysis
  • Data Mining
  • Databases
  • Department Of Defense
  • Expert Systems
  • Fungi
  • Genetic Algorithms
  • Information Systems
  • Information Theory
  • Machine Learning
  • Reasoning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms