Dunn, J.C.,
A Fuzzy Relative of the ISODATA Process and Its
Use in Detecting Compact Well-Separated Clusters,
Journal of Cybernetics(3), 1973, pp. 32-57.
Generalized the minimum variance to a fuzzy ISODATA method.
BibRef
7300
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
Saabin, M.J.,
Convergence and Consistency of Fuzzy C-Means / ISODATA Algorithms,
PAMI(9), No. 5, September 1987, pp. 661-668.
BibRef
8709
Venkateswarlu, N.B.,
Raju, P.S.V.S.K.,
Fast ISODATA clustering algorithms,
PR(25), No. 3, March 1992, pp. 335-342.
Elsevier DOI
0401
Dynamic clustering.
BibRef
Kittler, J.V.,
Pairman, D.,
Optimality of reassignment rules in dynamic clustering,
PR(21), No. 2, 1988, pp. 169-174.
Elsevier DOI
0309
ISODATA.
It is shown that contrary to popular belief these iterative clustering
algorithms do not guarantee that each stable partition is locally
optimal.
BibRef
Velasco, F.R.D.,
Thresholding Using the ISODATA Clustering Algorithm,
SMC(10), No. 11, November 1980, pp. 771-774.
ISODATA Clustering.
BibRef
8011
Kasif, S.[Simon], and
Rosenfeld, A.,
Pyramid Linking is a Special Case of ISODATA,
SMC(13), No. 1, January/February 1983, pp. 84-85.
ISODATA Clustering. The pyramid linking method is a special case of the ISODATA
clustering method, therefore is guaranteed to terminate.
See also Image Segmentation by Texture Using Pyramid Node Linking.
BibRef
8301
Lee, T.,
Richards, J.A.,
Piecewise Linear Classification Using Seniority Logic Committee
Methods, with Application to Remote Sensing,
PR(17), No. 4, 1984, pp. 453-464.
Elsevier DOI
0309
ISODATA Classification.
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.
Elsevier DOI
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 Link.
0201
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Nonparametric Clustering .