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Challenges in model‐based clustering
Author(s) -
Melnykov Volodymyr
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1248
Subject(s) - cluster analysis , exploratory data analysis , computer science , mixture model , statistical model , data mining , probabilistic logic , sample (material) , artificial intelligence , machine learning , chemistry , chromatography
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