2006 | OriginalPaper | Buchkapitel
Oriented k-windows: A PCA driven clustering method
verfasst von : D. K. Tasoulis, D. Zeimpekis, E. Gallopoulos, M. N. Vrahatis
Erschienen in: Advances in Web Intelligence and Data Mining
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In this paper we present the application of Principal Component Analysis (PCA) on subsets of the dataset to better approximate clusters. We focus on a specific density-based clustering algorithm,
k
-Windows, that holds particular promise for problems of moderate dimensionality. We show that the resulting algorithm, we call Oriented
k
-Windows (OkW), is able to steer the clustering procedure by effectively capturing several coexisting clusters of different orientation. OkW combines techniques from computational geometry and numerical linear algebra and appears to be particularly effective when applied on difficult datasets of moderate dimensionality.