Finding Hierarchies of Subspace Clusters
作者： Elke Achtert , Christian Böhm , Hans-Peter Kriegel , Peer Kröger , Ina Müller-Gorman
DOI: 10.1007/11871637_42
关键词: Cluster analysis 、 Computer science 、 Feature (machine learning) 、 Knowledge extraction 、 Pattern recognition 、 Linear subspace 、 Feature vector 、 Subspace topology 、 Artificial intelligence 、 Data set 、 Random subspace method
摘要: Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of original space. Those also called subspace clusters. In this paper, we propose algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies nested clusters, i.e. relationships lower-dimensional embedded within higher-dimensional Several comparative experiments using synthetic and real data sets show performance effectivity HiSC.
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