Minimum average cost clustering

Kiyohito Nagano; Yoshinobu Kawahara; Satoru Iwata

Minimum average cost clustering

Kiyohito Nagano, Yoshinobu Kawahara, Satoru Iwata

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 - 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/Territory	Canada
City	Vancouver, BC
Period	12/6/10 → 12/9/10

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

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.

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year = "2010",

language = "English",

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AU - Kawahara, Yoshinobu

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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.

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M3 - Conference contribution

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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

T2 - 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Y2 - 6 December 2010 through 9 December 2010

ER -

Minimum average cost clustering

Abstract

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All Science Journal Classification (ASJC) codes

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