PDDP - Principal Direction Divisive Partitioning Clustering 
  Algorithm 
    PDDP clusters a term-document matrix (tdm) using the 
    Principal Direction Divisive Partitioning clustering 
    algorithm [1, 2]. 
    CLUSTERS=PDDP(A, K, L) returns a cluster structure with 
    K clusters for the tdm A formed using information from 
    the first L principal components of the tdm. 
    [CLUSTERS, TREE_STRUCT]=PDDP(A, K, L) returns also the 
    full PDDP tree, while [CLUSTERS, TREE_STRUCT, S]=PDDP(A, 
    K, L) returns the objective function of PDDP. 
    PDDP(A, K, L, SVD_METHOD) defines the method used for the 
    computation of the PCA (svds - default - or propack), while 
    PDDP(A, K, L, SVD_METHOD, DSP) defines if results are to be 
    displayed to the command window (default 1) or not (0). 
 
    REFERENCES: 
    [1] D.Boley, Principal Direction Divisive Partitioning, Data 
    Mining and Knowledge Discovery 2 (1998), no. 4, 325-344.
    [2] D.Zeimpekis, E.Gallopoulos, PDDP(l): Towards a Flexible 
    Principal Direction Divisive Partitioning Clustering 
    Algorithmm, Proc. IEEE ICDM'03 Workshop on Clustering Large 
    Data Sets (Melbourne, Florida), 2003. 
 
  Copyright 2011 Dimitrios Zeimpekis, Eugenia Maria Kontopoulou, 
                 Efstratios Gallopoulos