View source: R/AgglomerativeNestingClustering.R
AgglomerativeNestingClustering | R Documentation |
Agglomerative hierarchical clustering (AGNES)of [Rousseeuw/Kaufman, 1990, pp. 199-252]
AgglomerativeNestingClustering(DataOrDistances, ClusterNo,
PlotIt = FALSE, Standardization = TRUE, ...)
DataOrDistances |
[1:n,1:d] matrix of dataset to be clustered. It consists of n cases or d-dimensional data points. Every case has d attributes, variables or features. Alternatively, symmetric [1:n,1:n] distance matrix |
ClusterNo |
A number k which defines k different clusters to be built by the algorithm.
if |
PlotIt |
Default: FALSE if |
Standardization |
|
... |
Further arguments to be set for the clustering algorithm, if not set, default arguments are used. |
List of
Cls |
[1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering. |
Dendrogram |
Dendrogram of hierarchical clustering algorithm |
Object |
Object defined by clustering algorithm as the other output of this algorithm |
Michael Thrun
[Rousseeuw/Kaufman, 1990] Rousseeuw, P. J., & Kaufman, L.: Finding groups in data, Belgium, John Wiley & Sons Inc., ISBN: 0471735787, doi 10.1002/9780470316801, Online ISBN: 9780470316801, 1990.
[Struyf et al., 1996] Struyf,A., Hubert, M. and Rousseeuw, Peter J.: Clustering in an Object-Oriented Environment, Journal of Statistical Software, Vol. 1, doi: 10.18637/jss.v001.i04, 1996.
[Struyf et al., 1997] Struyf, A., Hubert, M. and Rousseeuw, P.J.: Integrating Robust Clustering Techniques in S-PLUS, Computational Statistics and Data Analysis, Vol. 26, pp. 17–37, 1997.
agnes
data('Hepta')
CA=AgglomerativeNestingClustering(Hepta$Data,ClusterNo=7,PlotIt=FALSE)
## Not run:
ClusterDendrogram(CA$Dendrogram,7,main='AGNES clustering')
print(CA$Object)
plot(CA$Object)
## End(Not run)
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