title: Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.
abstract: Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.
authors.affiliation: Department of Educational Psychology, Ball State University Muncie, IN, USA.
authors.forename: Jocelyn H
authors.initials: JH
authors.lastname: Bolin
authors.affiliation: Department of Educational Psychology, Ball State University Muncie, IN, USA.
authors.forename: Julianne M
authors.initials: JM
authors.lastname: Edwards
authors.affiliation: Department of Educational Psychology, Ball State University Muncie, IN, USA.
authors.forename: W Holmes
authors.initials: WH
authors.lastname: Finch
authors.affiliation: Department of Educational Psychology, Ball State University Muncie, IN, USA.
authors.forename: Jerrell C
authors.initials: JC
authors.lastname: Cassady
keywords: classification; fuzzy clustering; k means clustering; perfectionism; profiles