By Topic

Methods of digraph representation and cluster analysis for analyzing free association

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
S. Miyamoto ; Inst. of Inf. Sci. & Electron., Tsukuba Univ., Ibaraki, Japan ; S. Suga ; K. Oi

A method for constructing two measures of association between a pair of words that distribute over a sequence is developed. The association measures are used for digraph representation and cluster analysis. In particular, study of a measure for cluster analysis leads to a new algorithm for hierarchical agglomerative clustering. The digraph representation and the cluster analysis are applied to data of free (psychological) association obtained from a questionnaire survey on the living environment of local residents. The two association measures are interpreted as estimates of probabilistic parameters. Hence, methods of hypothesis testing are developed for showing differences of structures of the free associations between two different populations. The results of the analysis of the association data are summarized into figures of digraphs and clusters that show structures of free associations of groups of people

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:20 ,  Issue: 3 )