Clustering of parameters on the basis of correlations: a comparative review of deterministic approaches

Article type: Research Article

Authors: Dzemyda, Gintautas

Affiliations: Institute of Mathematics and Informatics, Akademijos 4, 2600 Vilnius, Lithuania. E-mail: [email protected]

Abstract: The problem is to discover knowledge in the correlation matrix of parameters (variables) about their groups. Results that deal with deterministic approaches of parameter clustering on the basis of their correlation matrix are reviewed and extended. The conclusions on both theoretical and experimental investigations of various deterministic strategies in solving the problem of extremal parameter grouping are presented. The possibility of finding the optimal number of clusters is considered. The transformation of a general clustering problem into the clustering on the sphere and the relation between clustering of parameters on the basis of their correlation matrix and clustering of vectors (objects, cases) of an n-dimensional unit sphere are analysed.

Keywords: parameter clustering, data analysis, knowledge discovery

DOI: 10.3233/INF-1997-8105

Journal: Informatica, vol. 8, no. 1, pp. 83-118, 1997