Clustering Objects Generated by Linear Regression Models
Sheldon X. C. Lou, Jiong Jiang, Kenneth Keng · Journal of the American Statistical Association · 1993
This article describes a new clustering method designed for objects generated by a linear model with different states. The objective is to divide the objects into clusters, with each cluster containing only the objects taken when the system is in one particular state. The cluster centroids are matrices of predetermined rank and can be computed by the singular value decomposition (SVD) algorithm. The clustering problem is formulated as a combinatorial optimization problem. Two heuristic iterative algorithms that ensure the decrease of the objective function are then proposed. The simulation examples are given to show the effectiveness of the methodology.