CLUSTS

Mean shift mode-seeking clustering

    LAB = CLUSTS(A,K,MSIZE)
    LAB = A*CLUSTS(K,MSIZE)

Input
A	Dataset of M objects (rows).
K	Vector with desired numbers of clusters. Default is a set of N clusterings with numbers that naturally arise from the data.
MSIZE	Number of objects (M) above which the dataset is preclustered by
CLUSTM,	reducing it to MSIZE objects. Default MSIZE = 5000. Use
MSIZE	= inf to avoid perclustering.

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

This is a wrapper around MEANSHIFT, which is a wrapper around  MeanShiftCluster routine by Bryan Feldman and Bart Finkston. These  routines do not return clusterings with a preset number of clusters K.  The resulting multilevel clustering is first made nested by RECLUSTN.  After that the desired number of clusters is realized by RECLUSTK.

Example(s)

 randreset;                     % take care of reproducability
 data = gendatclust1(20000);    % generate 20000 objects in 10 clusters
                                % Run Mean Shift clustering
 lab = clusts(data,[2 5 10 18 30 50 100],2000);
                                % Show scatterplot for 10 clusters
 figure; scatn(lab(:,3),data,'Mean Shift'); 
 figure; clusteval(lab,data);   % Evaluation by active learning

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, cluste, clustf, clusth, clustk, clustkh, clustm, reclustn, preclust, clusteval, clustcerr, clustc,