A novel cluster analysis technique (PLS_Cluster) is proposed in this work. The method is based on the well-known PLS-regression procedure using a "self-organizing" mechanism to accomplish a clustering of the data. The implementation of this technique is straightforward and it provides several diagnostic insights into the reasons for clustering, by studying the obtained dendrogram, and by examining various statistical properties associated with the nodes. At each node it is possible to recover all the regression vectors common to PLS (loading X, loadings W, and B-coefficients) along with the scores X and scores y. This work presents the application of DiPLS_Cluster (a particular case of PLS_Cluster) to the analysis of several datasets and demonstrates the potentiality of this novel technique. (C) 2004 Elsevier B.V. All rights reserved.
机构:
Santa Clam Univ, Dept Comp Engn, 500 Camino Real, Santa Clara, CA 95053 USASanta Clam Univ, Dept Comp Engn, 500 Camino Real, Santa Clara, CA 95053 USA
Adolfsson, Andreas
Ackerman, Margareta
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Santa Clam Univ, Dept Comp Engn, 500 Camino Real, Santa Clara, CA 95053 USASanta Clam Univ, Dept Comp Engn, 500 Camino Real, Santa Clara, CA 95053 USA
Ackerman, Margareta
Brownstein, Naomi C.
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Florida State Univ, Dept Behav Sci & Social Med, 1115 West Call St, Tallahassee, FL 32306 USASanta Clam Univ, Dept Comp Engn, 500 Camino Real, Santa Clara, CA 95053 USA