Estimation and Selection in Regression Clustering

Authors

  • Guoqi Qian The University of Melbourne
  • Yuehua Wu York University

Keywords:

Regression clustering, Least squares, Model Selection

Abstract

Regression clustering is an important model-based clustering tool having applications in a variety of disciplines. It discovers and reconstructs the hidden structure for a data set which is a random sample from a population comprising a fixed, but unknown, number of sub-populations, each of which is characterized by a class-specific regression hyperplane. An essential objective, as well as a preliminary step, in most clustering techniques including regression clustering, is to determine the underlying number of clusters in the

data. In this paper, we briefly review regression clustering methods and discuss how to determine the underlying number of clusters by using model selection techniques, in particular, the information-based technique. A computing algorithm is developed for estimating the number of clusters and other parameters in regression clustering. Simulation studies are also provided to show the performance of the algorithm.

Author Biographies

  • Guoqi Qian, The University of Melbourne
    Department of Mathematics and Statistics
  • Yuehua Wu, York University
    Department of Mathematics and Statistics

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Published

2011-11-27

Issue

Section

Econometrics and Statistics

How to Cite

Estimation and Selection in Regression Clustering. (2011). European Journal of Pure and Applied Mathematics, 4(4), 455-466. https://www.ejpam.com/index.php/ejpam/article/view/1184