Performs the MST-kNN clustering algorithm which generate a clustering solution with automatic k determination using two proximity graphs: Minimal Spanning Tree (MST) and k-Nearest Neighbor (kNN) which are recursively intersected.
DataOrDistances: Either [1:n,1:n] symmetric distance matrix or [1:n,1:d] not symmetric data matrix of n cases and d variables
DistanceMethod: Optional distance method of data, default is euclid, see parDist for details
PlotIt: Default: FALSE, if TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
...: Optional, further arguments for mst.knn
Details
Does not work on Hepta with euclidean distances.
Returns
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.
Object: Object defined by clustering algorithm as the other output of this algorithm
References
[Inostroza-Ponta, 2008] Inostroza-Ponta, M.: An integrated and scalable approach based on combinatorial optimization techniques for the analysis of microarray data, University of Newcastle, ISBN, 2008