Abstract
Finite mixture models have been used extensively in clustering applications, where each component of the mixture distribution is assumed to represent an individual cluster. The simplest example describes each cluster in terms of a multivariate Gaussian density with various covariance structures. However, using finite mixture models as a clustering tool is highly flexible and allows for the specification of a wide range of statistical models to describe the data within each cluster. These include modelling each cluster using linear regression models, mixed effects models, generalized linear models, etc. This paper investigates using mixtures of orthogonal regression models to cluster biological data arising from a study of the sugarcane plant.
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Acknowledgements
Norma Bargary (nee Coffey) carried out this work at the National University of Ireland, Galway while supported by Science Foundation Ireland under Grant No. 07/MI/012 (BIO-SI project). The authors would also like to thank Dr. Anete P. Souza who carried out the laboratory work and is the co-ordinator of the project that produced the data analyzed.
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Bargary, N., Hinde, J., Garcia, A.A.F. (2014). Finite Mixture Model Clustering of SNP Data. In: MacKenzie, G., Peng, D. (eds) Statistical Modelling in Biostatistics and Bioinformatics. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-04579-5_11
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DOI: https://doi.org/10.1007/978-3-319-04579-5_11
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