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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/9162

Title: A new approach to cluster analysis: the clustering-function-based method
Authors: Li, Baibing
Keywords: Discriminant analysis
Gene expression data
Regression analysis
Sign eigenanalysis
Unsupervised learning
Issue Date: 2006
Publisher: Wiley-Blackwell © Royal Statistical Society
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.
Abstract: 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.
Description: 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
Version: Accepted for publication
DOI: 10.1111/j.1467-9868.2006.00549.x
URI: https://dspace.lboro.ac.uk/2134/9162
Publisher Link: http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2006.00549.x/abstract
ISSN: 1369-7412
Appears in Collections:Published Articles (Business School)

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