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

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