Clustering and Classification Using Mixture Models: Towards Big Data

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Modern scientific research produces ever-growing masses of data. Considering that scientists rely on statistics to interpret results, the availability of suitable statistical methods is essential. Unfortunately, some research questions in the big data era cannot be adequately answered due to the lack of appropriate statistical techniques. The proposed research will develop novel approaches to the labelling of observations as belonging to one of a number of groups; such approaches are called clustering or classification, depending on whether or not some of the points are labelled. The techniques that will be developed concern web streaming data and "long" longitudinal data. Furthermore, some work will be devoted to discovering a gold standard for classification using mixtures. These techniques, once developed, will be released for use by the broadest possible research community through packages for the R software. This work will extend research on model-based clustering and classification towards big data. The impact of this work will be felt in many disciplines in fields as diverse as biology, food science, security, and sociology.