14.2.15.1 ISODATA Clustering

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ISODATA Clustering. ISODATA is similar to K-Means, except ISODATA does not assume a given number of clusters.

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 .


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