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Towards Robust Arbitrarily Oriented Subspace Clustering

DATABASE SYSTEMS FOR ADVANCED APPLICATIONS (DASFAA 2019), PT I, (2019): 276-291

Cited by: 0|Views114
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Abstract

Clustering high-dimensional data is challenging since meaningful clusters usually hide in the arbitrarily oriented subspaces, and classical clustering algorithms like k-means tend to fail in such case. Subspace clustering has thus attracted growing attention in the last decade and many algorithms have been proposed such as ORCLUS and 4C. ...More

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