New index for clustering tendency.

  1. Forina, M. 2
  2. Lanteri, S. 2
  3. Esteban Díez, I. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 University of Genoa
    info

    University of Genoa

    Génova, Italia

    ROR https://ror.org/0107c5v14

Revista:
Analytica Chimica Acta

ISSN: 0003-2670

Ano de publicación: 2001

Volume: 446

Número: 1-2

Páxinas: 59-70

Tipo: Artigo

DOI: 10.1016/S0003-2670(01)01033-9 SCOPUS: 2-s2.0-0035914536 WoS: WOS:000172309400007 GOOGLE SCHOLAR

Outras publicacións en: Analytica Chimica Acta

Repositorio institucional: lock_openAcceso aberto Editor

Resumo

A new index for clustering tendency is described. The index is based on the frequency distribution of the lengths of the edges in the minimum spanning tree connecting the objects, compared with the probability distribution of the lengths of edges of the minimum spanning tree connecting the same number of objects described by variables extracted from the uniform distribution. The here suggested index shows some advantages when compared with the Hopkins original index and with its modification suggested by Fernández Pierna and Massart. It can be used both to detect clusters, to measure the degree of non-uniformity of a data set (as required in many cases of multivariate calibration and QSAR studies), and to detect outliers. © 2001 Elsevier Science B.V. All rights reserved.