Model-Based Clustering

By Paul David McNicholas. Journal of Classification, 2016

410 citations recorded by the source corpus.

The notion of defining a cluster as a component in a mixture model was put forth by Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an important subfield of classification. Considering the volume of work within this field over the past decade, which seems equal to all of that which went before, a review of work to date is timely. First, the definition of a cluster is discussed and some historical context for model-based clustering is provided. Then, starting with Gaussian mixtures, the evolution of model-based clustering is traced, from the famous paper by Wolfe in 1965 to work that is currently available only in preprint form. This review ends with a look ahead to the next decade or so.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/6aee33f19d324b7e5e02df4ad77f995b6e98bee3

Citations in the corpus

• Model-based clustering of high-dimensional data: A review (2014, neutral): “Let us however notice that several recent works focused on different distributions such as the skew normal [52], asymmetric Laplace [36] or t-distributions [3, 51, 68].”