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A new approach to cluster analysis: the clustering-function-based method

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journal contribution
posted on 2011-12-01, 14:51 authored by Baibing LiBaibing Li
The purpose of the paper is to present a new statistical approach to hierarchical cluster analysis with n objects measured on p variables. Motivated by the model of multivariate analysis of variance and the method of maximum likelihood, a clustering problem is formulated as a least squares optimization problem, simultaneously solving for both an n-vector of unknown group membership of objects and a linear clustering function. This formulation is shown to be linked to linear regression analysis and Fisher linear discriminant analysis and includes principal component regression for tackling multicollinearity or rank deficiency, polynomial or B-splines regression for handling non-linearity and various variable selection methods to eliminate irrelevant variables from data analysis. Algorithmic issues are investigated by using sign eigenanalysis.

History

School

  • Business and Economics

Department

  • Business

Published in

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Volume

68

Issue

3

Pages

457 - 475

Citation

LI, B., 2006. A new approach to cluster analysis: the clustering-function-based method. Journal of the Royal Statistical Society: Series B, 68 (3), pp. 457-476.

Publisher

Wiley-Blackwell © Royal Statistical Society

Version

  • AM (Accepted Manuscript)

Publication date

2006

Notes

This article was published in the serial, Journal of the Royal Statistical Society: Series B [Wiley-Blackwell © Royal Statistical Society]. The definitive version is available from: http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2006.00549.x/abstract

ISSN

1369-7412

Language

  • en