Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/103706
Type: Artigo de evento
Title: Data Clustering Using Self-organizing Maps Segmented By Mathematic Morphology And Simplified Cluster Validity Indexes: An Application In Remotely Sensed Images
Author: Goncalves M.L.
De Andrade Netto M.L.
Costa J.A.F.
Zullo Jr. J.
Abstract: 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.
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Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-40649091400&partnerID=40&md5=b12d9493309d01aede60c4327d48af21
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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