Functional MRI activation detection using genetic K-means clustering

Lin Shi; Pheng Ann Heng; Tien-Tsin Wong

Functional MRI activation detection using genetic K-means clustering

Lin Shi, Pheng Ann Heng, Tien-Tsin Wong

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

Abstract

We propose a novel clustering approach to fMRI activation detection using a genetic K-means algorithm, which is more likely to find a global optimal solution to the K-means clustering, and is independent of the initial assignments of the cluster centroids. The experiments show that the proposed method solves fMRI activation detection problem with higher accuracy than ordinary K-means clustering.

Original language	English
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1680-1685
Number of pages	6
ISBN (Print)	078039092X, 9780780390928
Publication status	Published - 2005
Externally published	Yes
Event	International Conference on Machine Learning and Cybernetics 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005 Conference number: 4th https://link.springer.com/book/10.1007/11739685 (Proceedings)

Conference

Conference	International Conference on Machine Learning and Cybernetics 2005
Abbreviated title	ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	18/08/05 → 21/08/05
Internet address	https://link.springer.com/book/10.1007/11739685 (Proceedings)

Keywords

Activation detection
fMRI
Genetic K-means clustering

Cite this

@inproceedings{b8b095a364084ec7924d27c0e802c892,

title = "Functional MRI activation detection using genetic K-means clustering",

abstract = "We propose a novel clustering approach to fMRI activation detection using a genetic K-means algorithm, which is more likely to find a global optimal solution to the K-means clustering, and is independent of the initial assignments of the cluster centroids. The experiments show that the proposed method solves fMRI activation detection problem with higher accuracy than ordinary K-means clustering.",

keywords = "Activation detection, fMRI, Genetic K-means clustering",

author = "Lin Shi and Heng, {Pheng Ann} and Tien-Tsin Wong",

year = "2005",

language = "English",

isbn = "078039092X",

pages = "1680--1685",

booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "International Conference on Machine Learning and Cybernetics 2005, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

url = "https://link.springer.com/book/10.1007/11739685",

}

Functional MRI activation detection using genetic K-means clustering. / Shi, Lin; Heng, Pheng Ann; Wong, Tien-Tsin.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. IEEE, Institute of Electrical and Electronics Engineers, 2005. p. 1680-1685.

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

TY - GEN

T1 - Functional MRI activation detection using genetic K-means clustering

AU - Shi, Lin

AU - Heng, Pheng Ann

AU - Wong, Tien-Tsin

N1 - Conference code: 4th

PY - 2005

Y1 - 2005

N2 - We propose a novel clustering approach to fMRI activation detection using a genetic K-means algorithm, which is more likely to find a global optimal solution to the K-means clustering, and is independent of the initial assignments of the cluster centroids. The experiments show that the proposed method solves fMRI activation detection problem with higher accuracy than ordinary K-means clustering.

AB - We propose a novel clustering approach to fMRI activation detection using a genetic K-means algorithm, which is more likely to find a global optimal solution to the K-means clustering, and is independent of the initial assignments of the cluster centroids. The experiments show that the proposed method solves fMRI activation detection problem with higher accuracy than ordinary K-means clustering.

KW - Activation detection

KW - fMRI

KW - Genetic K-means clustering

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M3 - Conference Paper

AN - SCOPUS:28444454501

SN - 078039092X

SN - 9780780390928

SP - 1680

EP - 1685

BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - International Conference on Machine Learning and Cybernetics 2005

Y2 - 18 August 2005 through 21 August 2005

ER -

Functional MRI activation detection using genetic K-means clustering

Abstract

Conference

Keywords

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Cite this