Application of Mixture Models to Large Datasets

Sharon X. Lee, Geoffrey McLachlan () and Saumyadipta Pyne ()
Additional contact information
Sharon X. Lee: University of Queensland, Department of Mathematics
Geoffrey McLachlan: University of Queensland, Department of Mathematics
Saumyadipta Pyne: Indian Institute of Public Health, Public Health Foundation of India

A chapter in Big Data Analytics, 2016, pp 57-74 from Springer

Abstract: Abstract Mixture distributions are commonly being applied for modelling and for discriminant and cluster analyses in a wide variety of situations. We first consider normal and t-mixture models. As they are highly parameterized, we review methods to enable them to be fitted to large datasets involving many observations and variables. Attention is then given to extensions of these mixture models to mixtures with skew normal and skew t-distributions for the segmentation of data into clusters of non-elliptical shape. The focus is then on the latter models in conjunction with the JCM (joint clustering and matching) procedure for an automated approach to the clustering of cells in a sample in flow cytometry where a large number of cells and their associated markers have been measured. For a class of multiple samples, we consider the use of JCM for matching the sample-specific clusters across the samples in the class and for improving the clustering of each individual sample. The supervised classification of a sample is also considered in the case where there are different classes of samples corresponding, for example, to different outcomes or treatment strategies for patients undergoing medical screening or treatment.

Keywords: Flow cytometry; Sample of cells; Multiple samples; Clustering of cells; Supervised classification of samples; Skew mixture models; EM algorithm (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-81-322-3628-3_4

Ordering information: This item can be ordered from
http://www.springer.com/9788132236283

DOI: 10.1007/978-81-322-3628-3_4

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().