Sean T Leonard1, Marcus Droege. 1. Nova Southeastern University College of Pharmacy, Fort Lauderdale, FL 33328, USA. LSean@nova.edu
Abstract
PURPOSE: Cluster analysis (CA) refers to a set of analytic procedures that reduce complex multivariate data into smaller subsets or groups. Compared with other data reduction methods, such as factor analysis, CA yields groupings that are based on the similarity of whole cases, as opposed to the individual variables that comprise those cases. CA represents a valuable analytic tool for the health sciences, and may be used to devise patient or consumer profiles, or in the development of classification systems or taxonomies. CA has become a more widely used analytic tool because before the advent of personal computers with high processing power, CA methods were too complex to be time efficient. Yet in the past few decades, interest in and the applied use of CA have advanced considerably. CA tools are now integrated into most popular statistical software packages and are therefore more accessible. METHODS: The authors provide a discussion of CA that seeks to introduce the various methods, issues, and considerations to the researcher who is largely unfamiliar with CA. A conceptual understanding of CA is guided through breaking down CA into a series of steps and issues to consider including composition of the dataset, selection of variables, decisions about standardizing variables, selecting a measure of association, selecting a clustering method, determining the number of clusters, and interpretation. RESULTS/ CONCLUSIONS: Because the range of CA methods is diverse, and because the steps within each method are so varied, an attempt to offer a complete "how-to" process in a single article is imprudent. Rather, the novice reader will be able to use this article as a starting point for conducting his or her own particular CA study.
PURPOSE: Cluster analysis (CA) refers to a set of analytic procedures that reduce complex multivariate data into smaller subsets or groups. Compared with other data reduction methods, such as factor analysis, CA yields groupings that are based on the similarity of whole cases, as opposed to the individual variables that comprise those cases. CA represents a valuable analytic tool for the health sciences, and may be used to devise patient or consumer profiles, or in the development of classification systems or taxonomies. CA has become a more widely used analytic tool because before the advent of personal computers with high processing power, CA methods were too complex to be time efficient. Yet in the past few decades, interest in and the applied use of CA have advanced considerably. CA tools are now integrated into most popular statistical software packages and are therefore more accessible. METHODS: The authors provide a discussion of CA that seeks to introduce the various methods, issues, and considerations to the researcher who is largely unfamiliar with CA. A conceptual understanding of CA is guided through breaking down CA into a series of steps and issues to consider including composition of the dataset, selection of variables, decisions about standardizing variables, selecting a measure of association, selecting a clustering method, determining the number of clusters, and interpretation. RESULTS/ CONCLUSIONS: Because the range of CA methods is diverse, and because the steps within each method are so varied, an attempt to offer a complete "how-to" process in a single article is imprudent. Rather, the novice reader will be able to use this article as a starting point for conducting his or her own particular CA study.