Abstract
In this paper, we propose a new spectral clustering algorithm relying on a simulated mixing process over a graph. In contrast to existing spectral clustering algorithms, our algorithm does not necessitate the computation of eigenvectors. Alternatively, our algorithm determines the equivalent of a linear combination of eigenvectors of the normalized similarity matrix, which are weighted by the corresponding eigenvalues obtained by the mixing process on the graph. We use the information gained from this linear combination of eigenvectors directly to partition the dataset into meaningful clusters. Simulations on real datasets show that our algorithm achieves better accuracy than standard spectral clustering methods as the number of clusters increase.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE 55th Conference on Decision and Control, CDC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 147-152 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509018376 |
| DOIs | |
| State | Published - Dec 27 2016 |
| Event | 55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: Dec 12 2016 → Dec 14 2016 |
Other
| Other | 55th IEEE Conference on Decision and Control, CDC 2016 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 12/12/16 → 12/14/16 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Decision Sciences (miscellaneous)
- Control and Optimization
Cite this
Large scale data clustering and graph partitioning via simulated mixing. / Bhatti, Shahzad; Beck, Carolyn; Nedich, Angelia.
2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 147-152 7798261.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Large scale data clustering and graph partitioning via simulated mixing
AU - Bhatti, Shahzad
AU - Beck, Carolyn
AU - Nedich, Angelia
PY - 2016/12/27
Y1 - 2016/12/27
N2 - In this paper, we propose a new spectral clustering algorithm relying on a simulated mixing process over a graph. In contrast to existing spectral clustering algorithms, our algorithm does not necessitate the computation of eigenvectors. Alternatively, our algorithm determines the equivalent of a linear combination of eigenvectors of the normalized similarity matrix, which are weighted by the corresponding eigenvalues obtained by the mixing process on the graph. We use the information gained from this linear combination of eigenvectors directly to partition the dataset into meaningful clusters. Simulations on real datasets show that our algorithm achieves better accuracy than standard spectral clustering methods as the number of clusters increase.
AB - In this paper, we propose a new spectral clustering algorithm relying on a simulated mixing process over a graph. In contrast to existing spectral clustering algorithms, our algorithm does not necessitate the computation of eigenvectors. Alternatively, our algorithm determines the equivalent of a linear combination of eigenvectors of the normalized similarity matrix, which are weighted by the corresponding eigenvalues obtained by the mixing process on the graph. We use the information gained from this linear combination of eigenvectors directly to partition the dataset into meaningful clusters. Simulations on real datasets show that our algorithm achieves better accuracy than standard spectral clustering methods as the number of clusters increase.
UR - http://www.scopus.com/inward/record.url?scp=85010715803&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85010715803&partnerID=8YFLogxK
U2 - 10.1109/CDC.2016.7798261
DO - 10.1109/CDC.2016.7798261
M3 - Conference contribution
AN - SCOPUS:85010715803
SP - 147
EP - 152
BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -