Challenges in model‐based clustering
Volodymyr Melnykov · Wiley Interdisciplinary Reviews Computational Statistics · 2013
Abstract 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