A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.
The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record
Department of Analytics, Information Systems, & Supply Chain, Florida State University.
Michael J
MJ
Brusco
Department of Psychological Sciences, University of Missouri.
Emilie
E
Shireman
Department of Psychological Sciences, University of Missouri.
Douglas
D
Steinley
D000465:Algorithms; D016000:Cluster Analysis; D005163:Factor Analysis, Statistical; D006801:Humans; D008960:Models, Psychological; D015233:Models, Statistical; D012107:Research Design