Methodology review: Clustering methods

Abstract

A review of clustering methodology is presented, with emphasis on algorithm performance and the resulting implications for applied research. After an overview of the clustering literature, the clustering process is discussed within a seven-step framework. The four major types of clustering methods can be characterized as hierarchical, partitioning, overlapping, and ordination algorithms. The validation of such algorithms refers to the problem of determining the ability of the methods to recover cluster configurations which are known to exist in the data. Validation approaches include mathematical derivations, analyses of empirical datasets, and monte carlo simulation methods. Next, interpretation and inference procedures in cluster analysis are discussed. inference procedures involve testing for significant cluster structure and the problem of determining the number of clusters in the data. The paper concludes with two sets of recommendations. One set deals with topics in clustering that would benefit from continued research into the methodology. The other set offers recommendations for applied analyses within the framework of the clustering process.