A procedure is developed for clustering objects in a low-dimensional subspace of the column space of an objects by variables data matrix. The method is based on the K-means criterion and seeks the subspace that is maximally informative about the clustering structure in the data. In this low-dimensional representation, the objects, the variables and the cluster centroids are displayed jointly. The advantages of the new method are discussed, an efficient alternating least-squares algorithm is described, and the procedure is illustrated on some artificial data.
CITATION STYLE
De Soete, G., & Carroll, J. D. (1994). K-means clustering in a low-dimensional Euclidean space (pp. 212–219). https://doi.org/10.1007/978-3-642-51175-2_24
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