Spectral clustering with inconsistent advice

Torn Coleman; James Saunderson; Anthony Wirth

Spectral clustering with inconsistent advice

Torn Coleman, James Saunderson, Anthony Wirth

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

Clustering with advice (often known as constrained clustering) has been a recent focus of the data mining community. Success has been achieved incorporating advice into the k-means and spectral clustering frameworks. Although the theory community has explored inconsistent advice, it has not yet been incorporated into spectral clustering. Extending work of De Bie and Cristianini, we set out a framework for finding minimum normalised cuts, subject to inconsistent advice.

Original language	English
Title of host publication	Proceedings of the 25th International Conference on Machine Learning
Pages	152-159
Number of pages	8
Publication status	Published - 2008
Externally published	Yes
Event	International Conference on Machine Learning 2008 - Helsinki, Finland Duration: 5 Jul 2008 → 9 Jul 2008 Conference number: 25th https://icml.cc/Conferences/2008/

Conference

Conference	International Conference on Machine Learning 2008
Abbreviated title	ICML 2008
Country	Finland
City	Helsinki
Period	5/07/08 → 9/07/08
Internet address	https://icml.cc/Conferences/2008/

Access to Document

Link to publication in Scopus

Cite this

@inproceedings{ba37835b1df0400ebd1f9e721f45fd7b,

title = "Spectral clustering with inconsistent advice",

abstract = "Clustering with advice (often known as constrained clustering) has been a recent focus of the data mining community. Success has been achieved incorporating advice into the k-means and spectral clustering frameworks. Although the theory community has explored inconsistent advice, it has not yet been incorporated into spectral clustering. Extending work of De Bie and Cristianini, we set out a framework for finding minimum normalised cuts, subject to inconsistent advice.",

author = "Torn Coleman and James Saunderson and Anthony Wirth",

year = "2008",

language = "English",

isbn = "9781605582054",

pages = "152--159",

booktitle = "Proceedings of the 25th International Conference on Machine Learning",

note = "International Conference on Machine Learning 2008, ICML 2008 ; Conference date: 05-07-2008 Through 09-07-2008",

url = "https://icml.cc/Conferences/2008/",

}

TY - GEN

T1 - Spectral clustering with inconsistent advice

AU - Coleman, Torn

AU - Saunderson, James

AU - Wirth, Anthony

N1 - Conference code: 25th

PY - 2008

Y1 - 2008

N2 - Clustering with advice (often known as constrained clustering) has been a recent focus of the data mining community. Success has been achieved incorporating advice into the k-means and spectral clustering frameworks. Although the theory community has explored inconsistent advice, it has not yet been incorporated into spectral clustering. Extending work of De Bie and Cristianini, we set out a framework for finding minimum normalised cuts, subject to inconsistent advice.

AB - Clustering with advice (often known as constrained clustering) has been a recent focus of the data mining community. Success has been achieved incorporating advice into the k-means and spectral clustering frameworks. Although the theory community has explored inconsistent advice, it has not yet been incorporated into spectral clustering. Extending work of De Bie and Cristianini, we set out a framework for finding minimum normalised cuts, subject to inconsistent advice.

UR - http://www.scopus.com/inward/record.url?scp=56449106588&partnerID=8YFLogxK

M3 - Conference Paper

AN - SCOPUS:56449106588

SN - 9781605582054

SP - 152

EP - 159

BT - Proceedings of the 25th International Conference on Machine Learning

T2 - International Conference on Machine Learning 2008

Y2 - 5 July 2008 through 9 July 2008

ER -