This paper presents a cluster analysis method which automatically finds the number of clusters as well as the partitioning of a data set without any type of interaction with the user. The data clustering is made using the Self-Organizing (or Kobonen) Map (SOM). Different partitions of the trained SOM are obtained from different segmentations of the U-matrix (a neuron-distance image) that are generated by means of mathematical morphology techniques. The different partitions of the trained SOM produce different partitions for the data set which are evaluated by cluster validity indexes. To reduce the computational cost of the cluster analysis process this work also proposes the simplification of cluster validity indexes using the statistical properties of the SOM. The proposed methodology is applied in the cluster analysis of remotely sensed images. © 2006 IEEE. 44214428Richards, J.A., Analysis of remotely sensed data: The formative decades and the future (2005) IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 422-432. , MarchSun, Z., Huang, D., Cheung, Y., Liu, J., Huang, G., Using FCMC, FVS, and PCA techniques for feature extraction of Multispectral images (2005) IEEE Geosc. and Rem. Sens. Lett, 2 (2), pp. 108-112. , AprilXu, R., Wunsch, D., Survey of clustering algorithms (2005) IEEE Trans. on Neural Networks, 16 (3), pp. 645-678. , MayTran, T.N., Wehrens, R., Buydes, L.M.C., Clustering multispectral images: A tutorial (2005) Chemometrics and Intelligent Laboratory Systems, 77, pp. 3-17Ball, G.H., Hall, D.J., (1965) ISODATA, a novel method of data analysis and pattern classification, , Stanford Research Institute, Menlo Park, CA, NTIS Report AD 699616Haykin, S., (1999) Neural Network: A Comprehensive Foundation, , New York: Prentice-Hall, 2nd editionCosta, J.A.F., Netto, M.L.A., Estimating the number of clusters in multivariate data by self-organizing maps (1999) International Journal of Neural Systems, 9, pp. 195-202J. A. F. Costa and M. L. A. Netto, Clustering of complex shaped data sets via kohonen maps and mathematical morphology, in Proceedings of the SPIE, Data Mining and Knowledge Discovery. B. Dasarathy (Ed.), 2001, 4384, pp. 16-27Gonçalves, M.L., Netto, M.L.A., Zullo, J., A neural architecture for the classification of remote sensing imagery with advanced learning algorithms (1998) Proc. of the IEEE Signal Processing Society Workshop, pp. 577-585M. L. Gonçalves, M. L. A. Netto, J. A. F. Costa and J. Zullo, Automatic remotely sensed data Clustering by tree-structured self-organizing, in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS'05, Korea, 2005Kohonen, T., (1997) Self-Organizing Maps, , 2nd Edition, Berlim: Springer VerlagUltsch, A., Self-organizing neural networks for visualization and classification (1993) Information and Classification, pp. 307-313. , O. Opitz et al, Eds, Berlin, Springer-Verlag, ppLampinen, J., Oja, E., Clustering properties of hierarchical self-organizing maps (1992) J. of Math. Im. and Vis, 2, pp. 261-272Murtagh, F., Interpreting the kohonen self-organizing feature map using contiguity-constrained clustering (1995) Pattern Recognition Letters, 16, pp. 399-408Kiang, M.Y., Extending the kohonen self-organizing map networks for clustering analysis (2001) Computational Statistics & Data Analysis, 38, pp. 161-180Vesanto, J., Alhoniemi, E., Clustering of the self-organizing map (2000) IEEE Trans. on Neural Networks, 11, pp. 586-600. , MayWu, S., Chow, T.W.S., Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intracluster density (2004) Pattern Recognition, 37, pp. 175-188Bleau, A., Leon, L.J., Watershed-based segmentation and region merging (2000) Comp. Vis. Image Underst, 77, pp. 317-370Bezdek, J.C., Pal, N.R., Some new indexes of cluster validity (1998) IEEE Trans. on Syst, Man and Cybern, 28, pp. 301-315Davies, D., Bouldin, D., A cluster separation measure (1979) IEEE Trans. Patt. Rec. and Mach. Intell, PAM1-1, pp. 224-227Pakhira, M.K., Bandyopedhyay, S., Maulik, U., Validity index for crisp and fuzzy clusters (2003) Pattern Recog, 37, pp. 487-501Halkidi, M., Vazirgiannis, M., Clustering validity assessment using multi representatives (2002) Proceedings of SETN Conference, , Thessaloniki, GreeceJI, M., Using fuzy sets to improve cluster labeling in unsupervised classification (2003) Int. J. of Remote Sensing, 24, pp. 657-671