Content area

Abstract

In many areas of psychology, one is interested in disclosing the underlying structural mechanisms that generated an object by variable data set. Often, based on theoretical or empirical arguments, it may be expected that these underlying mechanisms imply that the objects are grouped into clusters that are allowed to overlap (i.e., an object may belong to more than one cluster). In such cases, analyzing the data with Mirkin's additive profile clustering modelmay be appropriate. In this model: (1) each objectmay belong to no, one or several clusters, (2) there is a specific variable profile associated with each cluster, and (3) the scores of the objects on the variables can be reconstructed by adding the cluster-specific variable profiles of the clusters the object in question belongs to. Until now, however, no software program has been publicly available to perform an additive profile clustering analysis. For this purpose, in this article, the ADPROCLUS program, steered by a graphical user interface, is presented. We further illustrate its use by means of the analysis of a patient by symptom data matrix. [PUBLICATION ABSTRACT]

Details

Identifier / keyword
Title
ADPROCLUS: a graphical user interface for fitting additive profile clustering models to object by variable data matrices
Publication title
Volume
43
Issue
1
Pages
56-65
Number of pages
10
Publication year
2011
Publication date
Mar 2011
Publisher
Springer Nature B.V.
Place of publication
New York
Country of publication
Netherlands
Publication subject
e-ISSN
15543528
Source type
Scholarly Journal
Language of publication
English
Document type
Feature, Journal Article
Document feature
Diagrams; Equations; Tables; Graphs; References
Accession number
21287114
ProQuest document ID
920259701
Document URL
https://www.proquest.com/scholarly-journals/adproclus-graphical-user-interface-fitting/docview/920259701/se-2?accountid=208611
Copyright
Copyright Springer Science & Business Media Mar 2011
Last updated
2025-11-16
Database
ProQuest One Academic