Abstract
In our era of data deluge, clustering algorithms that do not scale well with the dramatically increasing number of data have to be reconsidered. Spectral clustering, while powerful, is computationally and memory demanding, even for high performance computers. Capitalizing on the relationship between spectral clustering and kernel k-means, the present paper introduces a randomized algorithm for identifying communities in large-scale graphs based on a random sketching and validation approach, that enjoys reduced complexity compared to the clairvoyant spectral clustering. Numerical tests on synthetic and real data demonstrate the potential of the proposed approach.
Original language | English (US) |
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Title of host publication | 2015 49th Annual Conference on Information Sciences and Systems, CISS 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781479984282 |
DOIs | |
State | Published - Apr 15 2015 |
Event | 2015 49th Annual Conference on Information Sciences and Systems, CISS 2015 - Baltimore, United States Duration: Mar 18 2015 → Mar 20 2015 |
Publication series
Name | 2015 49th Annual Conference on Information Sciences and Systems, CISS 2015 |
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Other
Other | 2015 49th Annual Conference on Information Sciences and Systems, CISS 2015 |
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Country/Territory | United States |
City | Baltimore |
Period | 3/18/15 → 3/20/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Sketch and validate
- Spectral clustering
- community identification