A Case Study of two Clustering Methods based on Maximum Likelihood
Statistica NeerlandicaPublished 1 June 1979
S. Gànesalingam, Geoffrey J. McLachlan
Citations18
SJR quartileQ2
SJR score0.55
SNIP1.31
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TL;DR
Two commonly used clustering methods based on maximum likelihood are considered in the context of the classification problem where observations of unknown origin belong to one of two possible populations.
Abstract
Abstract Two commonly used clustering methods based on maximum likelihood are considered in the context of the classification problem where observations of unknown origin belong to one of two possible populations. The basic assumptions and associated properties of the two methods are contrasted and illustrated by their application to some medical data. Also, the problem of updating an allocation procedure is considered.
Keywords
Computer ScienceMathematics
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