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