Development of Cluster Analysis Methods Suitable for Student Opinion Data.

Abstract

The Naval Postgraduate School's Student Opinion Form data were subjected to study through the use of two cluster analysis techniques: (1) K-MEANS partitioning method and (2) Chernoff's FACES. Much developmental work was performed to tailor these methods to the special requirements of the data set. A thorough multivariate statistical review provided the basis for choosing optimality criteria and distance functions for use in the MIKCA (Multivariate Iterative K-MEANS Clustering Algorithm). Alterations were made to the computer code to allow the analysis to include the effect of class size on cluster membership. Use of the linear discriminant function aided in identifying variables for use in constructing features of the computer-drawn faces. This approach to the Chernoff's FACES technique shows promise but needs further development. A principal components analysis of the data showed it to be essentially one dimensional. Partitioning the data into four clusters shows that the scoring of the courses varies inversely with class size. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1979
Accession Number
ADA068544

Entities

People

  • Joel Weston Aiken

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Active Denial System
  • Algorithms
  • Computer Programs
  • Computers
  • Data Mining
  • Data Science
  • Data Sets
  • Databases
  • Discriminant Analysis
  • Electrical Engineering
  • Information Science
  • Operating Systems
  • Plastic Explosives
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Students

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