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Clustering with position-specific constraints on variance: applying redescending M-estimators to label-free LC-MS data analysis.
|Publication Type||Journal Article|
|Authors||Frühwirth, R., Mani D. R., and Pyne S.|
|Abstract||Clustering is a widely applicable pattern recognition method for discovering groups of similar observations in data. While there are a large variety of clustering algorithms, very few of these can enforce constraints on the variation of attributes for data points included in a given cluster. In particular, a clustering algorithm that can limit variation within a cluster according to that cluster's position (centroid location) can produce effective and optimal results in many important applications ranging from clustering of silicon pixels or calorimeter cells in high-energy physics to label-free liquid chromatography based mass spectrometry (LC-MS) data analysis in proteomics and metabolomics.|
|Year of Publication||2011|
|Date Published (YYYY/MM/DD)||2011/08/31|