MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions

By Chris S. Wallace, David L. Dowe. 2000

190 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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

In Statistics and Probability

Citations in the corpus

Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, methodology): “Then, an implementation of the Snob clustering algorithm is applied to the feature vector, to find the optimal grouping of time series (Wallace and Dowe, 2000).”
Latent Class Cluster Analysis (2021, background): “Several authors have proposed using latent class models for dealing with unobserved heterogeneity in covariance-structure analysis ( Arminger and Stein 1997; Dolan and Van der Maas 1997; Jedidi et. al. 1997).”
Bayesian Artificial Intelligence (2004, methodology): “We applied the SNOB probabilistic classification program [289], based on the information theoretic Minimum Message Length (MML) [290] to the data from 2437 testpapers in different forms: 1.”
Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection (2001, methodology): “Wallace & Dowe (2000) employ MML for clustering mixtures of multi-state, Poisson, von Mises, and Gaussian data.”
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, methodology): “Then, the “Snob” clustering algorithm, a mixture model based on the Minimum Message Length (MML) concept, introduced by Wallace and Dowe (2000), is applied to the extracted feature vector, to obtain the clusters.”
Simultaneous feature selection and clustering using mixture models (2004, methodology): “[18], which is based on the MML criterion [61, 60 ].”