DCLUSTM

Dissimilarity based multi-level clustering by kNN mode-seeking.

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

A kNN modeseeking method is used to assign each object to its nearest  density mode. Object densities are related to the distances to neighbors.  Modes are determined by recusively jumping to objects in the neighborhood  with the highest density. As many clusters are found as there are objects  that are the mode in their own neighborhood.

Modeseeking clustering does not return a predefined number of clusters.  Determining a clustering with exactly K clusters is done by RECLUSTK. It  might reduce the perfomance.

This routine is based on the same algorithm as CLUSTM (for feature based  data), which uses MODECLUST and MODECLUSTF. For reasons of speed it is  implemented in another way, so results may differ slightly.

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

Reference(s)

Cheng, Y. "Mean shift, mode Seeking, and clustering", IEEE Transactions on PAMI, vol. 17, no. 8, pp. 790-799, 1995.

R.P.W. Duin, A.L.N. Fred, M. Loog, and E. Pekalska, Mode Seeking Clustering by KNN and Mean Shift Evaluated, Proc. SSPR & SPR 2012, LNCS, vol. 7626, Springer, 2012, 51-59.

See also

datasets, mappings, dclustk, dclusth, dcluste, modeclust, modeclustf, kclust, reclustn, clusteval, clustcerr, clustc,