Redaktion: K. Sigmund, G. Greschonig (Univ. Wien, Strudlhofgasse ...

164 Wahrscheinlichkeitstheorie, Statistik
Numerical Taxonomy Methods for Statistical Data Processing
TIBERIU POSTELNICU
Zentrum für Mathematische Statistik der Akademie
Calea 13 Septembrie nr. 13,76100 Bukarest 5, Romania
tposteln@k.ro
http://www.csm.ro
The purpose of numerical taxonomy can be briefly defined as the construction of
objective clusters of units by means of a quantitative measure of their affinity. Its
name comes from the fact that the first methods were proposed for, and essentially
applied to, the biological classification.
Numerical taxonomy methods present a very powerful multiple comparison instrument.
More general, cluster analysis is the name given to various procedures
whereby a set of individuals or units, termed as “Operational Taxonomic
Units” (OTU). Techniques of cluster analysis can be applied in different fields
of medicine: the recognition of various clinical forms of a disease, separation of
distinctive racial groups, treatment of quantitative biogeographical data, etc.
An important case for statistical data processing deals with OTUs described by
binary attributes. Homogeneities for binary and for ordered multistates data are
presented. Methods of automatic classification are described and two types of
homogeneities for the classification in biology and the genetics of the human populations
are given.
The new extension concerns the inference in contingency table and it is applicable
in any field. The connection between numerical taxonomy, one side, and the
cluster analysis, as well as the discriminant analysis, on the other side, is useful to
be considered.
[1] Dragomirescu L., Postelnicu T., (1994), Specific numerical taxonomy methods
in biological classification. In “Statistical Tools in Human Biology”.,
World Scientific, 31-46.
[2] Buser M.W., Baroni-Urbani C., (1982), A direct nondimensional clustering
method for binary data. Biometrics, 38, 351-360.
[3] Sneath P.H.A., Sokal R.R., (1973), Numerical taxonomy. San Francisco
Freeman.
[4] Vichi,M. (1998), Principal classification analysis: a method for generating
consensus dendrograms and its application to three way data. Computational
Statistics & Data Analysis, 27, 3, 311-331.