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Cluster Analysis via Density Functions: Amelioration of a Lower Bound of the Parameter
Author(s) -
Kopp B.
Publication year - 1984
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710260418
Subject(s) - mathematics , cluster (spacecraft) , upper and lower bounds , multivariate statistics , statistics , value (mathematics) , statistical physics , computer science , mathematical analysis , physics , programming language
This paper refers to an earlier investigation on Cluster Analysis procedures based on general empirical density functions, in which the number of classes is controled by a positive parameter λ: the number of clusters, m , increases as λ →∞. Since there is no functional relationship between m and λ, upper and lower bounds of the parameter are of interest (see KOPP, 1976a). We now give a better value for the lower bound of λ in the case of the multivariate normal distribution.