pddp

Principal Direction Divisive Partitioning algorithm
[IDX,T] = PDDP(X, K, VARARGIN) 

 [IDX, T] = PDDP(X, K) produces a divisive hierarchical clustering of the
 N-by-D data matrix (X). This algorithm uses a hierarchy of binary partitions
 each splitting the observations by first projecting onto the first principal
 component and then splitting at the mean of the projected data

  [IDX,T] = PDDP(X,K) returns the cluster assignment, IDX, and the  binary 
  tree (T) containing the cluster hierarchy

  [IDX, T] = PDDP(X, 'PARAM1',val1, 'PARAM2',val2, ...) specifies optional parameters
  in the form of Name,Value pairs. 

  'split_index' - Criterion determining which cluster to split    next
    Function Handle: index = split_index(v, X, pars)
            (v: projection vector, X:data matrix, pars: parameters structure)
    Cluster with MAXIMUM INDEX is split at each step of the algorithm
    Two standard choices of split index can be enabled by setting 'split_index' to 
    one of the strings below:
        + 'scatter': Split cluster with largest total scatter value
        + 'size':    Split largest cluster
    (default: split_index = 'scatter')

  'minsize' - Minimum cluster size (integer)
    (default minsize = 1)

  'labels' - true cluster labels. Only used for performance assessment

Reference:
D. Boley. Principal Direction Divisive Partitioning. Data Mining and Knowledge Discovery, 2(4):325-344, 1998.