Hierarchical Subspace Clustering
نویسنده
چکیده
It is well-known that traditional clustering methods considering all dimensions of the feature space usually fail in terms of efficiency and effectivity when applied to high-dimensional data. This poor behavior is based on the fact that clusters may not be found in the high-dimensional feature space, although clusters exist in subspaces of the feature space. To overcome these limitations of traditional clustering methods, several methods for subspace clustering have been proposed recently. Subspace clustering algorithms aim at automatically identifying lower dimensional subspaces of the feature space in which clusters exist. There exist two types of subspace clustering algorithms: Algorithms for detecting clusters in axis-parallel subspaces and, as an extension, algorithms for finding clusters in subspaces which are arbitrarily oriented. Generally, the subspace clusters may be hierarchically nested, i.e., several subspace clusters of low dimensionality may form a subspace cluster of higher dimensionality. Since existing subspace clustering methods are not able to detect these complex structures, hierarchical approaches for subspace clustering have to be applied. The goal of this dissertation is to develop new efficient and effective methods for hierarchical subspace clustering by identifying novel challenges for the hierarchical approach and proposing innovative and solid solutions for these challenges. The first Part of this work deals with the analysis of hierarchical subspace clusters in axis-parallel subspaces. Two new methods are proposed
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