Finite mixture models and model-based clustering

By Volodymyr Melnykov, Ranjan Maitra. 2010

289 citations recorded by the source corpus.

Finite mixture models have a long history in statistics, hav- ing been used to model pupulation heterogeneity, generalize distributional assumptions, and lately, for providing a convenient yet formal framework for clustering and classication. This paper provides a detailed review into mixture models and model-based clustering. Recent trends in the area, as well as open problems are also discussed.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c05ff19149fc4e7fd3afea5a69e07c0905bc51bd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Benedikt Pfülb
2. Alexander Rainer Tassilo Gepperth
3. Surajit Ray
4. Holly
5. Dan Ren
6. Raudino
7. Sprogis
8. Kate

In Statistics and Probability

1. Dan Ren
2. Alexander Rainer Tassilo Gepperth
3. Surajit Ray
4. Holly
5. Raudino
6. Sprogis
7. Kate
8. Benedikt Pfülb

Citations in the corpus

• Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces (2019, methodology): “GMMs have been used in a wide range of scenarios, e.g., Melnykov et al. (2010).”
• On the upper bound of the number of modes of a multivariate normal mixture (2012, background): “A comprehensive summary of the above results are available in Frühwirth-Schnatter (2006) and a recent review paper by Melnykov and Maitra (2010).”
• Home range size of adult Indo-Pacific bottlenose dolphins (Tursiops aduncus) in a coastal and estuarine system is habitat and sex-specific (2016, methodology): “These ideas were already technically well-developed (Melnykov and Maitra 2010).”