Challenges in model‐based clustering

Model‐based clustering is an increasingly popular area of cluster analysis that relies on probabilistic description of data by means of finite mixture models. Mixture distributions prove to be a powerful technique for modeling heterogeneity in data. In model‐based clustering, each data group is seen as a sample from one or several mixture components. Despite attractive interpretation, model‐based clustering poses many challenges. This paper discusses some of the most important problems a researcher might encounter while applying the model‐based cluster analysis. WIREs Comput Stat 2013, 5:135–148. doi: 10.1002/wics.1248 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical and Graphical Methods of Data Analysis > Density Estimation