MinimaxLinkageClustering {FCPS} | R Documentation |
Minimax Linkage Hierarchical Clustering
Description
In the minimax linkage hierarchical clustering every cluster has an associated prototype element that represents that cluster [Bien/Tibshirani, 2011].
Usage
MinimaxLinkageClustering(DataOrDistances, ClusterNo = 0,
DistanceMethod="euclidean", ColorTreshold = 0,...)
Arguments
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 build by the algorithm. |
DistanceMethod |
See |
ColorTreshold |
Draws cutline w.r.t. dendogram y-axis (height), height of line as scalar should be given |
... |
In case of plotting further argument for |
Value
List of
Cls |
If, ClusterNo>0: [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. Otherwise for ClusterNo=0: NULL |
Dendrogram |
Dendrogram of hierarchical clustering algorithm |
Object |
Ultrametric tree of hierarchical clustering algorithm |
Author(s)
Michael Thrun
References
[Bien/Tibshirani, 2011] Bien, J., and Tibshirani, R.: Hierarchical Clustering with Prototypes via Minimax Linkage, The Journal of the American Statistical Association, Vol. 106(495), pp. 1075-1084, 2011.
See Also
Examples
data('Hepta')
out=MinimaxLinkageClustering(Hepta$Data,ClusterNo=7)