mdh

Minimum Density Hyperplane
[IDX,SOL] = MDH(X, VARARGIN)

  [IDX,SOL] = MDH(X) bi-partitions the points in the N-by-D data matrix X with
  the hyperplane that minimises the density on a hyperplane criterion
  (computed from one-dimensional projections of the data). 

  MDH returns a vector IDX containing the binary cluster assignment and a
  Minimum Density Hyperplane (mdhp) object SOL. (If S initial projection
  vectors are specified S nchp hyperplanes are returned: see v0 option)

  [IDX,SOL] = MDH(X, 'PARAM1',val1, 'PARAM2',val2, ...) specifies optional parameters
  in the form of name/value pairs. 

  OPTIONAL PARAMETERS:
  'v0' - D-by-S matrix of S initial projection vectors
       (default: v0 = pca(X,'NumComponents',1) : First principal component of X)

  'bandwidth' - Bandwidth parameter (default: H = MULT * N^(-0.2) STD(X*PC1), where PC1 is
        the 1st principal component)

  'alphamin' - The minimum ALPHA over which MDHs are sought: 
    [mean(X*V) - ALPHA*std(X*V), mean(X*V) + ALPHA*std(X*V)]. 
    ALPHA starts from (alphamin) and increases by 0.1 every (maxit) iterations until (alphamax) is reached.
    (default: 0)

  'alphamax' - The maximum ALPHA over which MDHs are sought is 
    [mean(X*V) - ALPHA*std(X*V), mean(X*V) + ALPHA*std(X*V)]. 
    ALPHA starts from (alphamin) and increases by 0.1 every (maxit) iterations until (alphamax) is reached.
    (default: 1)

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

  'maxit' - Number of BFGS iterations to perform for each value of alpha (default: 50)

  'ftol' - Stopping criterion for change in objective function value over consecutive iterations
    (default: 1.e-5)

  'verb' - Verbosity. Values greater than 0 enable visualisation during execution
        (default: 0)

  'labels' - true cluster assignment. Enables the computation of performance over 
    successive iterations and a better visualisation of how clusters are split

  'colours' - Matrix containing colour specification for observations in different clusters
    Number of rows must be equal to the number of true clusters (if 'labels' has been specified) or equal to 2.

Reference:
N.G. Pavlidis, D.P. Hofmeyr and S.K. Tasoulis. Minimum density hyperplanes.
Journal of Machine Learning Research, 17(156):1-33, 2016.
http://jmlr.org/papers/v17/15-307.html.