Abstract
A number of objective functions in clustering problems can be described with submodular functions. In this paper, we introduce the minimum average cost criterion, and show that the theory of intersecting submodular functions can be used for clustering with submodular objective functions. The proposed algorithm does not require the number of clusters in advance, and it will be determined by the property of a given set of data points. The minimum average cost clustering problem is parameterized with a real variable, and surprisingly, we show that all information about optimal clusterings for all parameters can be computed in polynomial time in total. Additionally, we evaluate the performance of the proposed algorithm through computational experiments.
| Original language | English |
|---|---|
| Title of host publication | Advances in Neural Information Processing Systems 23 |
| Subtitle of host publication | 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 |
| Publication status | Published - Dec 1 2010 |
| Externally published | Yes |
| Event | 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 - Vancouver, BC, Canada Duration: Dec 6 2010 → Dec 9 2010 |
Publication series
| Name | Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 |
|---|
Conference
| Conference | 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 12/6/10 → 12/9/10 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Information Systems
Cite this
Minimum average cost clustering. / Nagano, Kiyohito; Kawahara, Yoshinobu; Iwata, Satoru.
Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010. 2010. (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Minimum average cost clustering
AU - Nagano, Kiyohito
AU - Kawahara, Yoshinobu
AU - Iwata, Satoru
PY - 2010/12/1
Y1 - 2010/12/1
N2 - A number of objective functions in clustering problems can be described with submodular functions. In this paper, we introduce the minimum average cost criterion, and show that the theory of intersecting submodular functions can be used for clustering with submodular objective functions. The proposed algorithm does not require the number of clusters in advance, and it will be determined by the property of a given set of data points. The minimum average cost clustering problem is parameterized with a real variable, and surprisingly, we show that all information about optimal clusterings for all parameters can be computed in polynomial time in total. Additionally, we evaluate the performance of the proposed algorithm through computational experiments.
AB - A number of objective functions in clustering problems can be described with submodular functions. In this paper, we introduce the minimum average cost criterion, and show that the theory of intersecting submodular functions can be used for clustering with submodular objective functions. The proposed algorithm does not require the number of clusters in advance, and it will be determined by the property of a given set of data points. The minimum average cost clustering problem is parameterized with a real variable, and surprisingly, we show that all information about optimal clusterings for all parameters can be computed in polynomial time in total. Additionally, we evaluate the performance of the proposed algorithm through computational experiments.
UR - http://www.scopus.com/inward/record.url?scp=84860627339&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860627339&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84860627339
SN - 9781617823800
T3 - Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
BT - Advances in Neural Information Processing Systems 23
ER -