Curve Clustering with Random Effects Regression Mixtures

Scott Gaffney, Padhraic Smyth
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, PMLR R4:101-108, 2003.

Abstract

In this paper we address the problem of clustering sets of curve or trajectory data generated by groups of objects or individuals. The focus is to model curve data directly using a set of model-based curve clustering algorithms referred to as mixtures of regressions or regression mixtures. The proposed methodology is based on extension to regression mixtures that we call random effects regression mixtures which combines linear random effects models with standard regression mixtures. We develop a general expectationmaximization (EM) algorithm using maximum a posteriori (MAP) estimation for random effects regression mixtures and demonstrate how this technique can be applied to the problem of clustering cyclone data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR4-gaffney03a, title = {Curve Clustering with Random Effects Regression Mixtures}, author = {Gaffney, Scott and Smyth, Padhraic}, booktitle = {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics}, pages = {101--108}, year = {2003}, editor = {Bishop, Christopher M. and Frey, Brendan J.}, volume = {R4}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r4/gaffney03a/gaffney03a.pdf}, url = {https://proceedings.mlr.press/r4/gaffney03a.html}, abstract = {In this paper we address the problem of clustering sets of curve or trajectory data generated by groups of objects or individuals. The focus is to model curve data directly using a set of model-based curve clustering algorithms referred to as mixtures of regressions or regression mixtures. The proposed methodology is based on extension to regression mixtures that we call random effects regression mixtures which combines linear random effects models with standard regression mixtures. We develop a general expectationmaximization (EM) algorithm using maximum a posteriori (MAP) estimation for random effects regression mixtures and demonstrate how this technique can be applied to the problem of clustering cyclone data.}, note = {Reissued by PMLR on 01 April 2021.} }
Endnote
%0 Conference Paper %T Curve Clustering with Random Effects Regression Mixtures %A Scott Gaffney %A Padhraic Smyth %B Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2003 %E Christopher M. Bishop %E Brendan J. Frey %F pmlr-vR4-gaffney03a %I PMLR %P 101--108 %U https://proceedings.mlr.press/r4/gaffney03a.html %V R4 %X In this paper we address the problem of clustering sets of curve or trajectory data generated by groups of objects or individuals. The focus is to model curve data directly using a set of model-based curve clustering algorithms referred to as mixtures of regressions or regression mixtures. The proposed methodology is based on extension to regression mixtures that we call random effects regression mixtures which combines linear random effects models with standard regression mixtures. We develop a general expectationmaximization (EM) algorithm using maximum a posteriori (MAP) estimation for random effects regression mixtures and demonstrate how this technique can be applied to the problem of clustering cyclone data. %Z Reissued by PMLR on 01 April 2021.
APA
Gaffney, S. & Smyth, P.. (2003). Curve Clustering with Random Effects Regression Mixtures. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R4:101-108 Available from https://proceedings.mlr.press/r4/gaffney03a.html. Reissued by PMLR on 01 April 2021.

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