PDDP_OPTCUT_2MEANS - Hybrid Principal Direction Divisive 
  Partitioning Clustering Algorithm and k-means
    PDDP_OPTCUT_2MEANS clusters a term-document matrix (tdm) 
    using a combination of the Principal Direction Divisive 
    Partitioning clustering algorithm [1] and k-means [2]. 
    CLUSTERS=PDDP_OPTCUT_OPTCUT_2MEANS(A, K) returns a cluster 
    structure with K clusters for the tdm A. 
    [CLUSTERS, TREE_STRUCT]=PDDP_OPTCUT_2MEANS(A, K) returns also 
    the full PDDP tree, while [CLUSTERS, TREE_STRUCT, S]=
    PDDP_OPTCUT_2MEANS(A, K) returns the objective function of 
    PDDP. 
    PDDP_OPTCUT_2MEANS(A, K, SVD_METHOD) defines the method used 
    for the computation of the PCA (svds - default - or propack). 
    PDDP_OPTCUT_2MEANS(A, K, SVD_METHOD, DSP) defines if results 
    are to be displayed to the command window (default 1) or not 
    (0). Finally, PDDP_OPTCUT_2MEANS(A, K, SVD_METHOD, DSP, EPSILON)
    defines the termination criterion value for the k-means 
    algorithm. 
 
    REFERENCES: 
    [1] D.Boley, Principal Direction Divisive Partitioning, Data 
    Mining and Knowledge Discovery 2 (1998), no. 4, 325-344.
    [2] D.Zeimpekis, E.Gallopoulos, k-means Steering of Spectral 
    Divisive Clustering Algorithms, Proc. of Text Mining Workshop, 
    Minneapolis, 2007.
 
  Copyright 2011 Dimitrios Zeimpekis, Eugenia Maria Kontopoulou, 
                 Efstratios Gallopoulos