Dissimilarity based random clustering
LAB = DCLUSTR(D,K)
LAB = D*DCLUSTR(K)
| 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.|
| 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.|
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.
datasets, mappings, kmeans, prkmeans, clustk, dclustm, dclusth, dcluste, dclustf, clusteval, clustcerr, clustc,
|This file has been automatically generated. If badly readable, use the help-command in Matlab.|