DCLUSTR

Dissimilarity based random clustering

    LAB = DCLUSTR(D,K)
    LAB = D*DCLUSTR(K)

Input
D	Square dissimilarity matrix, size M*M
K	Scalar or a vector of length N with desired numbers of clusters.  Default is a set of N clusterings with numbers that naturally  arise from the data.

Output
LAB	M*N array with the results of the multi-level clusterings for the
M	objects. The columns refer to the N clusterings. They yield for  the objects the prototype indices of the clusters they belong to.

Description

The dataset A with M rows (objects) is clustered by selecting a random  set of K prototypes out of A. All other objects are assigned to the  nearest prototype. In case K is a set, larger sets of prototypes will  contain the smaller ones.

Clusterings can be evaluated by CLUSTEVAL, CLUSTCERR or CLUSTC on the  basis of (some) true labels.

See also

datasets, mappings, kmeans, prkmeans, clustk, dclustm, dclusth, dcluste, dclustf, clusteval, clustcerr, clustc,