som_cllinkage

sC = som_cllinkage(sM,varargin)

SOM_CLLINKAGE Make a hierarchical linkage of the SOM map units.

 sC = som_cllinkage(sM, [[argID,] value, ...])
  
  sC = som_cllinkage(sM);
  sC = som_cllinkage(D,'complete');
  sC = som_cllinkage(sM,'single','ignore',find(~som_hits(sM,D)));
  sC = som_cllinkage(sM,pdist(sM.codebook,'mahal'));
  som_clplot(sC); 

  Input and output arguments ([]'s are optional):
   sM       (struct) map or data struct to be clustered
            (matrix) size dlen x dim, a data set: the matrix must not
                     contain any NaN's!
   [argID,  (string) See below. The values which are unambiguous can 
    value]  (varies) be given without the preceeding argID.

   sC       (struct) a clustering struct with e.g. the following fields
                     (for more information see SOMCL_STRUCT)
     .base  (vector) if base partitioning is given, this is a newly 
                     coded version of it so that the cluster indices
                     go from 1 to the number of clusters. 
     .tree  (matrix) size clen-1 x 3, the linkage info
                     Z(i,1) and Z(i,2) hold the indeces of clusters 
                     combined on level i (starting from bottom). The new
                     cluster has index dlen+i. The initial cluster 
                     index of each unit is its linear index in the 
                     original data matrix. Z(i,3) is the distance
                     between the combined clusters. See LINKAGE
                     function in the Statistics Toolbox.
     
 Here are the valid argument IDs and corresponding values. The values 
 which are unambiguous (marked with '*') can be given without the
 preceeding argID.
   'topol'   *(struct) topology struct
   'connect' *(string) 'neighbors' or 'any' (default), whether the
                       connections should be allowed only between 
                       neighbors or between any vectors
              (matrix) size dlen x dlen indicating the connections
                       between vectors
   'linkage' *(string) the linkage criteria to use: 'single' (the
                       default), 'average', 'complete', 'centroid', or 'ward' 
   'dist'     (matrix) size dlen x dlen, pairwise distance matrix to 
                       be used instead of euclidian distances
              (vector) as the output of PDIST function
              (scalar) distance norm to use (default is euclidian = 2)
   'mask'     (vector) size dim x 1, the search mask used to 
                       weight distance calculation. By default 
                       sM.mask or a vector of ones is used.
   'base'     (vector) giving the base partitioning of the data: 
                       base(i) = j denotes that vector i belongs to
                       base cluster j, and base(i) = NaN that vector
                       i does not belong to any cluster, but should be
                       ignored. At the beginning of the clustering, the 
                       vector of each cluster are averaged, and these
                       averaged vectors are then clustered using 
                       hierarchical clustering.
   'ignore'   (vector) units to be ignored (in addition to those listed
                       in base argument)
   'tracking' (scalar) 1 or 0: whether to show tracking bar or not (default = 0)

 Note that if 'connect'='neighbors' and some vector are ignored (as denoted
 by NaNs in the base vector), there may be areas on the map which will
 never be connected: connections across the ignored map units simply do not
 exist. In such a case, the neighborhood is gradually increased until 
 the areas can be connected.

 See also KMEANS_CLUSTERS, LINKAGE, PDIST, DENDROGRAM.