DataOrDistances: Either a [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.
or a [1:n,1:n] symmetric distance matrix.
ClusterNo: Numeric indicating number to cluster to find in Tree/ Dendrogramm in case of Type="Hierachical" or numer of cluster to use in Type="kmeans"
Type: (optional) Char selecting methods Hierarchical or kmeans. Default: "Hierarchical"
PlotIt: (optional) Boolean. Default = FALSE = No plotting performed.
Silent: (optional) Boolean: print output or not (Default = FALSE = no output)
NoPerms: (optional), numeric scalar, Number of permutations.
Wbounds: (optional) numeric vector, range of tuning parameters to consider. This is the L1 bound on w, the feature weights [Witten/Tibshirani, 2010].
...: Further arguments passed on to sparcl HierarchicalSparseCluster or KMeansSparseCluster depending on Type.
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
Tree: Object Tree if Type="Hierachical" is used.
References
[Witten/Tibshirani, 2010] Witten, D. and Tibshirani, R.: A Framework for Feature Selection in Clustering. Journal of the American Statistical Association, Vol. 105(490), pp. 713-726, 2010.
Author(s)
Quirin Stier, Michael Thrun
Note
Quality of clustering results varies between sparse hierarchical if data is given in comparison to the case that distances are given.