MODECLUST

KNN mode-seeking clustering

    [LAB,NNLAB,NDIST] = MODECLUST(A,K,DIST,NEST)

Input
A	Dataset of M objects
K	Vector with numbers of neighbours to search for local mode.  Default: smart sampling.
DIST	Distance function (name or handle) or mapping to be used  for clustering (optional; default: @DISTM).  If DIST is a function it is expected to take two double arrays  as input arguments: if D = DIST(A1, A2) then SIZE(D) is
[SIZE(A1,	1) SIZE(A2, 1)]. If DIST is a mapping then it should  be possible to use it like D = A1*(A2*DIST), i.e. it has to be  a trainable mapping and it should be able automatically convert  double arrays to datasets. E.g., for PROXM it is possible to  call MODECLUST(A,K,PROXM), but for DISTM we need to use
MODECLUST(A,K,@DISTM)	or MODECLUST(A,K,'distm').
NEST	Logical, if TRUE the output set of clusterings (columns of LAB) will be made nested by RECLUSTN. Default: TRUE

Output
LAB	Indices of mode samples, size [M,N] with K the number of  clusterings (NUMEL(K)).
NNLAB	[M,1} vector with indices of nearest neighbors.
NDIST	Total number of distance calculations.

Description

A NN modeseeking method is used to assign objects to their nearest mode.  Object densities are defined by one over the distance to the K-th nearest  neighbour. Clusters are defined by recursively jumping for every object  to the object with the highest density in the local neighborhood.

K can be a vector of neighborhood sizes, which is much faster.  Default K: a set of values determined by a geometric series.

Computing time is in the order of M^2 seconds, with M the number of  objects in A times 1000.

Reference(s)

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.

Example(s)

 delfigs
 a = gendatm(5000);      % generate 5000 objects in 8 classes
 [lab,k] = modeclust(a); 
 for j=1:size(lab,2)
   nclust = numel(unique(lab(:,j)));
   if nclust < 20 & nclust %gt 1
     figure; scattern(prdataset(a,lab(:,j)));
     title(['K = ' int2str(k(j)) ' --%gt ' int2str(nclust) ' Clusters']);
   end
 end
 showfigs

See also

mappings, datasets, distm, proxm, clustm, dclustm, modeclust_batch, modeclustf, reclustn,