Internal function of Hierarchical Clusterering of Data
Internal function of Hierarchical Clusterering of Data
Please use HierarchicalClustering. Hierarchical cluster analysis on a set of dissimilarities and methods for analyzing it. Uses stats package function 'hclust'.
Data: [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.
ClusterNo: A number k which defines k different clusters to be build by the algorithm.
Type: Methode der Clusterung: "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid".
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. dendrogram y-axis (height), height of line as scalar should be given
Fast: If TRUE and fastcluster installed, then a faster implementation of the methods above can be used
Cls: [1:n] classification vector for coloring of dendrogram in plot
...: 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 for ClusterNo=0: NULL
Dendrogram: Dendrogram of hierarchical clustering algorithm
Object: Ultrametric tree of hierarchical clustering algorithm