SKMEANS - Spherical k-Means Clustering Algorithm 
    SKMEANS clusters a term-document matrix using the Spherical 
    k-means clustering algorithm [1]. CLUSTERS=SKMEANS(A, C, K, 
    TERMINATION) returns a cluster structure with K clusters 
    for the term-document matrix A using as initial centroids 
    the columns of C (initialized randomly when it is empty). 
    TERMINATION defines the termination method used in spherical 
    k-means ('epsilon' stops iteration when objective function 
    increase falls down a user defined threshold - see OPTIONS 
    input argument - while 'n_iter' stops iteration when a user 
    defined number of iterations has been reached). 
    [CLUSTERS, Q]=SKMEANS(A, C, K, TERMINATION) returns also
    the vector of objective function values for each iteration 
    and [CLUSTERS, Q, C]=SKMEANS(A, C, K, TERMINATION) returns 
    the final centroid vectors. 
    SKMEANS(A, C, K, TERMINATION, OPTIONS) defines optional 
    parameters: 
        - OPTIONS.iter: Number of iterations (default 10).
        - OPTIONS.epsilon: Value for epsilon convergence 
          criterion (default 1).
        - OPTIONS.dsp: Displays results (default 1) or not (0) 
          to the command window.
 
    REFERENCES: 
    [1] I. S. Dhillon and D. M. Modha, "Concept Decompositions 
    for Large Sparse Text Data using Clustering", Machine 
    Learning, 42:1, pages 143-175, Jan, 2001.
 
  Copyright 2011 Dimitrios Zeimpekis, Eugenia Maria Kontopoulou, 
                 Efstratios Gallopoulos