Cluster Analysis via Density Functions: Amelioration of a Lower Bound of the Parameter | Zendy

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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.

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