NDLI: A method to estimate the statistical confidence of cluster separation

Content Provider	Springer Nature Link
Author	Gerstengarbe, F. W. Werner, P. C.
Copyright Year	1997
Abstract	Cluster analysis contains several multivariate methods for the separation of patterns (clusters). The definition of the optimum or universally best cluster analysis is an unresolved issue. Three methods are of special importance: 1. The statistical confidence of cluster separation. 2. The definition of the optimal number of clusters. 3. The description of the internal cluster structure. Two new methods addressing these problems are presented. On the basis of nonhierarchical minimum-distance cluster analysis a new method is described that allows a separation of clusters in a statistically well-founded way. This method solves problems one and two. Using a newly developed special rank-sum analysis, a solution to the third problem is possible. An example shows the practicability of the proposed procedures.
Starting Page	103
Ending Page	110
Page Count	8
File Format	PDF
ISSN	0177798X
Journal	Theoretical and Applied Climatology
Volume Number	57
Issue Number	1-2
e-ISSN	14344483
Language	English
Publisher	Springer-Verlag
Publisher Date	1997-01-01
Publisher Place	Vienna
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Meteorology/Climatology Atmospheric Protection/Air Quality Control/Air Pollution Climate Change Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science

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A method to estimate the statistical confidence of cluster separation