Monte Carlo validation of two genetic clustering algorithms

Cluster analysis refers to a type of statistical method designed to identify homogeneous groups within complex, multivariate data sets. In this study two newly developed genetic cluster analysis algorithms, GENCLUS and GENCLUS+, were validated by comparing their performance against that of three popular clustering techniques (Ward's method, K-means w/ random seeds, K-means w/Ward's centroids) and in an elaborate Monte Carlo study. Additionally, the ability of GENCLUS+ to determine the correct number of clusters was compared against that of three conventional procedures (Calinski and Harabasz, C-index, trace W). GENCLUS and GENCLUS+ achieved Rand recovery values slightly inferior to those of conventional methods. However, GENCLUS+ appeared to perform better than conventional methods in an empirical analysis, and genetic method solutions appear to possess high internal cohesion and external isolation. The mixed results are interpreted as an indication of a discrepancy between cluster theory and conventional data generation techniques.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Psychology

Department dc:contributor.department

Psychology

Grantor dc:publisher

Virginia Polytechnic Institute and State University

Year dc:date.issued

1993

Author dc:creator

• Cowgill, Marc

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10919/109241

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/109241