Minimum average cost clustering

Kiyohito Nagano, Yoshinobu Kawahara, Satoru Iwata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

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 languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 23
Subtitle of host publication24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
Publication statusPublished - Dec 1 2010
Externally publishedYes
Event24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 - Vancouver, BC, Canada
Duration: Dec 6 2010Dec 9 2010

Publication series

NameAdvances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Conference

Conference24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
CountryCanada
CityVancouver, BC
Period12/6/1012/9/10

Fingerprint

Costs
Real variables
Polynomials
Experiments

All Science Journal Classification (ASJC) codes

  • Information Systems

Cite this

Nagano, K., Kawahara, Y., & Iwata, S. (2010). Minimum average cost clustering. In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010 (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010).

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 proceedingConference contribution

Nagano, K, Kawahara, Y & Iwata, S 2010, Minimum average cost clustering. in Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010, 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010, Vancouver, BC, Canada, 12/6/10.
Nagano K, Kawahara Y, Iwata S. Minimum average cost clustering. In 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).
Nagano, Kiyohito ; Kawahara, Yoshinobu ; Iwata, Satoru. / Minimum average cost clustering. 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).
@inproceedings{652bf1f4aade4a3480ace32fd6a459c3,
title = "Minimum average cost clustering",
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.",
author = "Kiyohito Nagano and Yoshinobu Kawahara and Satoru Iwata",
year = "2010",
month = "12",
day = "1",
language = "English",
isbn = "9781617823800",
series = "Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010",
booktitle = "Advances in Neural Information Processing Systems 23",

}

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 -