Data Clustering using Self-Organizing Maps segmented by Mathematic Morphology and Simplified Cluster Validity Indexes: an application in remotely sensed images

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 Kohonen) 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.

[1]  John A. Richards,et al.  Analysis of remotely sensed data: the formative decades and the future , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  Fionn Murtagh,et al.  Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering , 1995, Pattern Recognit. Lett..

[4]  M. Vazirgiannis,et al.  Clustering validity assessment using multi representatives , 2002 .

[5]  Geoffrey H. Ball,et al.  ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .

[6]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[7]  José Alfredo Ferreira Costa,et al.  Clustering of complex shaped data sets via Kohonen maps and mathematical morphology , 2001, Data Mining and Knowledge Discovery: Theory, Tools, and Technology.

[8]  Lutgarde M. C. Buydens,et al.  Clustering multispectral images: a tutorial , 2005 .

[9]  Minhe Ji,et al.  Using fuzzy sets to improve cluster labelling in unsupervised classification , 2003 .

[10]  James C. Bezdek,et al.  Some new indexes of cluster validity , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[11]  L. Joshua Leon,et al.  Watershed-Based Segmentation and Region Merging , 2000, Comput. Vis. Image Underst..

[12]  Melody Y. Kiang,et al.  Extending the Kohonen self-organizing map networks for clustering analysis , 2002 .

[13]  M.L. de Andrade Netto,et al.  A neural architecture for the classification of remote sensing imagery with advanced learning algorithms , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

[14]  De-Shuang Huang,et al.  Using FCMC, FVS, and PCA techniques for feature extraction of multispectral images , 2005, IEEE Geosci. Remote. Sens. Lett..

[15]  Ujjwal Maulik,et al.  Validity index for crisp and fuzzy clusters , 2004, Pattern Recognit..

[16]  E. Oja,et al.  Clustering Properties of Hierarchical Self-Organizing Maps , 1992 .

[17]  Tommy W. S. Chow,et al.  Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density , 2004, Pattern Recognit..

[18]  José Alfredo Ferreira Costa,et al.  Estimating the Number of Clusters in Multivariate Data by Self-Organizing Maps , 1999, Int. J. Neural Syst..

[19]  A. Ultsch,et al.  Self-Organizing Neural Networks for Visualisation and Classification , 1993 .

[20]  Jouko Lampinen,et al.  Clustering properties of hierarchical self-organizing maps , 1992, Journal of Mathematical Imaging and Vision.

[21]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  José Alfredo Ferreira Costa,et al.  Automatic remotely sensed data clustering by tree-structured self-organizing maps , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..