PDDP_OPTCUTPD - Hybrid Principal Direction Divisive 
  Partitioning Clustering Algorithm and k-means
    PDDP_OPTCUTPD clusters a term-document matrix (tdm) using 
    a combination of the Principal Direction Divisive 
    Partitioning clustering algorithm [1, 2] and k-means [3].
    CLUSTERS=PDDP_OPTCUT_OPTCUTPD(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_OPTCUTPD(A, K, L) returns 
    also the full PDDP tree, while [CLUSTERS, TREE_STRUCT, S]=
    PDDP_OPTCUTPD(A, K, L) returns the objective function of 
    PDDP. 
    PDDP_OPTCUTPD(A, K, L, SVD_METHOD) defines the method used 
    for the computation of the PCA (svds - default - or 
    propack). Finally, PDDP_OPTCUTPD(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. 
    [3] 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
					
				

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