Conceptual Clustering Using Relational Information.

Abstract

Work in conceptual clustering has focused on creating classes from objects with a fixed set of features, such as color or size. In this paper we describe a system which uses relations between the objects being clustered as well as features of the objects to form a hierarchy tree of classes. Unlike previous conceptual clustering systems, this algorithm can define new attributes. Using relational information the system is able to find object classifications not possible with conventional conceptual clustering methods. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 23, 1986
Accession Number
ADA170874

Entities

People

  • Bernd Nordhausen

Organizations

  • University of California, Irvine

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Animals
  • Applied Computer Science
  • Artificial Intelligence
  • Birds
  • California
  • Classification
  • Clustering
  • Computations
  • Computer Science
  • Computers
  • Food Chains
  • Genetics
  • Hierarchies
  • Information Science
  • Locomotion
  • Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design