Dissimilarity based clustering by the FFT algorithm.
LAB = DCLUSTF(D,K)
The FFT (Farthest First Traversal) algorithm is used to find a set of K prototypes. All other objects are assigned to the nearest prototype. In LAB for all objects the index in D of the nearest cluster prototype is returned. If K is a vector LAB has length(K) columns, returning a multilevel clustering.
Note that the FFT algorithm is not really a clustering procedure but just a fast way to split the data in spatially distant parts.
Clusterings can be evaluated by CLUSTEVAL, CLUSTCERR or CLUSTC on the basis of (some) true labels.