New Developments in COSA: Clustering Objects on Subsets of Attributes
Jacqueline J. Meulman, Jerome H. Friedman · 2004
Abstract. The motivation for clustering objects on subsets of attributes (COSA) was given by consideration of data where the number of attributes is much larger than the number of objects. Obvious application is in systems biology (genomics, proteomics, and metabolomics). When we have a large numbers of attributes, ob-jects might cluster on some attributes, and be far apart on all others. Common data analysis approaches in systems biology are to cluster the attributes first, and only after having reduced the original many-attribute data set to a much smaller one, one tries to cluster the objects. The problem here, of course, is that we would like to select those attributes that discriminate most among the objects (so we have to do this while regarding all attributes multivariately), and it is usually not good enough to inspect each attribute univariately. Therefore, two tasks have to be carried out simultaneously: cluster the objects into homogeneous groups, while se-lecting different subsets of variables (one for each group of objects). The attribute subset for any discovered group may be completely, partially or nonoverlapping with those for other groups. The notorious local optima problem is dealt with by