Abstract
Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of the original feature space. Those clusters are also called subspace clusters. In this paper, we propose the algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies of nested subspace clusters, i.e. the relationships of lower-dimensional subspace clusters that are embedded within higher-dimensional subspace clusters. Several comparative experiments using synthetic and real data sets show the performance and the effectivity of HiSC.
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Keywords
- Subspace Dimensionality
- Preference Vector
- Subspace Cluster
- Hierarchical Cluster Algorithm
- Original Feature Space
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© 2006 Springer-Verlag Berlin Heidelberg
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Achtert, E., Böhm, C., Kriegel, HP., Kröger, P., Müller-Gorman, I., Zimek, A. (2006). Finding Hierarchies of Subspace Clusters. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science(), vol 4213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871637_42
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DOI: https://doi.org/10.1007/11871637_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45374-1
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