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DiPCA\ Cluster: An optimal alternative to DiPLS\ Cluster for unsupervised classification by Verron Thomas; Sabatier Robert; Joffre Richard is a Chemistry article available to read on EtoBox.

What is DiPCA\ Cluster: An optimal alternative to DiPLS\ Cluster for unsupervised classification about?

Unsupervised cluster analysis is frequently used to explore the structure of large datasets. This paper introduces a new method, DiPCA\_Cluster, which is an optimal alternative to DiPLS\_Cluster with two additional refinements. Indeed, DiPLS\_Cluster is not optimal as it does not converge to a unique solution (it depends on the algorithm initialization) and moreover, provides dendrograms which are not very meaningful. The method proposed in this paper, DiPCA\_Cluster is optimal with regards to the chosen criterion, inertia. Furthermore, at each step of the analysis, the data have to be split into two groups thanks to the values of a 0/1 vector. In our method, unlike DiPLS\_Cluster, this split is based on the use of a threshold value which is more in accordance with the general problem, in this goal, two proposals are suggested. Finally, whereas DiPLS\_Cluster set up the dendrogram by using the number of iterations, DiPCA\_Cluster uses the total variance explained by each principal component within each group which is more justified and which allows to find out dendrograms very close to the ones provided by Ward criterion.

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Author

Verron Thomas; Sabatier Robert; Joffre Richard

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0169-7439)

Published

2008

Language

EN

Field

Chemistry (Physical Sciences)