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Subspace Clustering and Some Soft Variants

  • Marie-Jeanne LesotEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11940)

Abstract

Subspace clustering is an unsupervised machine learning task that, as clustering, decomposes a data set into subgroups that are both distinct and compact, and that, in addition, explicitly takes into account the fact that the data subgroups live in different subspaces of the feature space. This paper provides a brief survey of the main approaches that have been proposed to address this task, distinguishing between the two paradigms used in the literature: the first one builds a local similarity matrix to extract more appropriate data subgroups, whereas the second one explicitly identifies the subspaces, so as to dispose of more complete information about the clusters. It then focuses on soft computing approaches, that in particular exploit the framework of the fuzzy set theory to identify both the data subgroups and their associated subspaces.

Keywords

Machine learning Unsupervised learning Subspace clustering Soft computing Fuzzy logic 

Notes

Acknowledgements

I wish to thank Arthur Guillon and Christophe Marsala with whom I started exploring the domain of subspace clustering.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sorbonne Université, CNRS, LIP6ParisFrance

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