| Applications of a new subspace clustering algorithm (COSA) in medical systems biology (2007) | |||||||||||
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| Design and Operation Power Systems with Large Amounts Wind Power Production IEA collaboration Hannele Holttinen Peter Meibom Cornel Ensslin Lutz Hofmann John McCann Jan Pierik John Olav Tande Espen Hagstr Ana Estanqueiro Hortensia Amaris Lennart der Goran Strbac Brian Parsons VTT Technical Research Centre Finland Ris National Laboratories Denmark ISET Germany Netz Germany SEI Ireland ECN The Netherlands SINTEF Norway Statkraft Norway INETI Portugal University Carlos III Spain KTH Sweden Imperial College NREL USA mail hannele holttinen vtt Abstract New collaboration Design and Operation Power Systems with Large Amounts Wind Power Production has been formed IEA Wind The task will collect and share information the experience gained and the studies made power system impacts wind power and review methodologies tools and data used This paper outlines the power system impacts wind power the national studies published and going and describes the goals the international collaboration There are dozens studies made and ongoing related cost wind integration however the results are not easy compare depth review the studies needed draw conclusions the range integration costs for wind power State the art review process will seek for reasons behind the wide range results for costs wind integration definitions for wind penetration reserves and costs different power system and load characteristics and operational rules underlying assumptions variability wind etc Introduction The existing targe. A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies. | |||||||||||
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