DataOrDistances: [1:n,1:d] matrix of dataset to be clustered. It consists of n cases of 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 parDist, for example 'euclidean','mahalanobis','manhatten' (cityblock),'fJaccard','binary', 'canberra', 'maximum'. Any unambiguous substring can be given.
ColorTreshold: Draws cutline w.r.t. dendogram y-axis (height), height of line as scalar should be given
Data: [1:n,1:d] data matrix in the case that DataOrDistances is missing and partial matching does not work.
...: In case of plotting further argument for plot, see as.dendrogram
Returns
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 ClusterNo=0: NULL
Dendrogram: Dendrogram of hierarchical clustering algorithm
Object: Ultrametric tree of hierarchical clustering algorithm
References
[Szekely/Rizzo, 2005] Szekely, G. J. and Rizzo, M. L.: Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method, Journal of Classification, 22(2) 151-183.http://dx.doi.org/10.1007/s00357-005-0012-9, 2005.