PDDP_2MEANS - Hybrid Principal Direction Divisive Partitioning 
  Clustering Algorithm and k-means
    PDDP_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_2MEANS(A, K) returns a cluster structure with K 
    clusters for the tdm A. 
    [CLUSTERS, TREE_STRUCT]=PDDP_2MEANS(A, K) returns also the 
    full PDDP tree, while [CLUSTERS, TREE_STRUCT, S]=PDDP_2MEANS(A, 
    K) returns the objective function of PDDP. 
    PDDP_2MEANS(A, K, SVD_METHOD) defines the method used for the 
    computation of the PCA (svds - default - or propack). 
    PDDP_2MEANS(A, K, SVD_METHOD, DSP) defines if results are to 
    be displayed to the command window (default 1) or not (0). 
    Finally, PDDP_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