Subspace clustering

@article{Kriegel2012SubspaceC,
  title={Subspace clustering},
  author={Hans-Peter Kriegel and Peer Kr{\"o}ger and Arthur Zimek},
  journal={Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
  year={2012},
  volume={2}
}
Subspace clustering refers to the task of identifying clusters of similar objects or data records (vectors) where the similarity is defined with respect to a subset of the attributes (i.e., a subspace of the data space). The subspace is not necessarily (and actually is usually not) the same for different clusters within one clustering solution. In this article, the problems motivating subspace clustering are sketched, different definitions and usages of subspaces for clustering are described… Expand
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Large-Scale Sparse Subspace Clustering Using Landmarks
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References

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TLDR
This survey presents enhanced approaches to subspace clustering by discussing the problems they are solving, their cluster definitions and algorithms, and the related works in high-dimensional clustering. Expand
Less is More: Non-Redundant Subspace Clustering
TLDR
This work is presented on identifying non-redundant, relevant subspace clusters which reduce the result set to a manageable size and discusses techniques for evaluating, visualizing and exploring subspace clusterings, and proposes some directions for future work. Expand
ASCLU : Alternative Subspace Clustering
Finding groups of similar objects in databases is one of the most important data mining tasks. Recently, traditional clustering approaches have been extended to generate alternative clusteringExpand
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A survey of the various subspace clustering algorithms along with a hierarchy organizing the algorithms by their defining characteristics is presented, comparing the two main approaches using empirical scalability and accuracy tests and discussing some potential applications where sub space clustering could be particularly useful. Expand
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TLDR
This paper evaluates systematically state-of-the-art subspace and projected clustering techniques under a wide range of experimental settings and makes recommendations regarding what type of techniques are suitable for what kind of problems. Expand
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TLDR
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TLDR
A dynamic subspace clustering method is proposed, which extends the density based projected clustering algorithm PreDeCon for dynamic data and efficiently examines only those clusters that might be affected due to the population update. Expand
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TLDR
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