Reference number : GC-2017-Q1-02539, amendment number : , agreement type : G, recipient legal name : McNicholas, Paul (McMaster University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : 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., program purpose french : 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., agreement title english : Clustering and Classification Using Mixture Models: Towards Big Data, agreement title french : Clustering and Classification Using Mixture Models: Towards Big Data, agreement number : RGPIN, agreement value : 215000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada