Selim, S.Z., and
Ismail, M.A.,
On the Local Optimality of the Fuzzy ISODATA Clustering Algorithm,
PAMI(8), No. 2, March 1986, pp. 284-288.
See also K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality.
See also Fuzzy C-Means: Optimality of solutions and effective termination of the algorithm.
BibRef
8603
Venkateswarlu, N.B.,
Raju, P.S.V.S.K.,
Fast isodata clustering algorithms,
PR(25), No. 3, March 1992, pp. 335-342.
WWW Version.
0401
BibRef
Kittler, J.V.,
Pairman, D.,
Optimality of reassignment rules in dynamic clustering,
PR(21), No. 2, 1988, pp. 169-174.
WWW Version.
0309ISODATA.
It is shown that contrary to popular belief these iterative clustering
algorithms do not guarantee that each stable partition is locally
optimal.
BibRef
Carman, C.S.[Charles S.],
Merickel, M.B.[Michael B.],
Supervising ISODATA with an information theoretic stopping rule,
PR(23), No. 1-2, 1990, pp. 185-197.
WWW Version.
0401
BibRef
Huang, K.Y.[Kai-Yi],
A Synergistic Automatic Clustering Technique (SYNERACT )
for Multispectral Image Analysis,
PhEngRS(68), No. 1, January 2002, pp. 33-40.
A new effective synergistic automatic clustering technique serves as a
substitute for
ISODATA when applied to remote sensing image analysis with a large data set.
WWW Version.
0201
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Nonparametric Clustering .