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