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Published in:
2006 | OriginalPaper | Chapter
Kernel PCA as a Visualization Tools for Clusters Identifications
Authors : Alissar Nasser, Denis Hamad, Chaiban Nasr
Published in: Artificial Neural Networks – ICANN 2006
Publisher: Springer Berlin Heidelberg
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Kernel PCA has been proven to be a powerful technique as a nonlinear feature extractor and a pre-processing step for classification algorithms. KPCA can also be considered as a visualization tool; by looking at the scatter plot of the projected data, we can distinguish the different clusters within the original data. We propose to use visualization given by KPCA in order to decide the number of clusters. K-means clustering algorithm on both data and projected space is then applied using synthetic and real datasets. The number of clusters discovered by the user is compared to the Davies-Bouldin index originally used as a way of deciding the number of clusters.