Interval-valued centroids in K-Means algorithms

Benjamine Nordin; Chenyi Hu; Bernard Chen; Victor S. Sheng

doi:10.1109/ICMLA.2012.87

Interval-valued centroids in K-Means algorithms

Benjamine Nordin, Chenyi Hu, Bernard Chen, Victor S. Sheng

Computer Science

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

6 Scopus citations

Abstract

The K-Means algorithms are fundamental in machine learning and data mining. In this study, we investigate interval-valued rather than commonly used point-valued centroids in the K-Means algorithm. Using a proposed interval peak method to select initial interval centroids, we have obtained overall quality improvement of clusters on a set of test problems in the Fundamental Clustering Problem Suite (FCPS).

Original language	English
Title of host publication	Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages	478-481
Number of pages	4
DOIs	https://doi.org/10.1109/ICMLA.2012.87
State	Published - 2012
Event	11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States Duration: Dec 12 2012 → Dec 15 2012

Publication series

Name	Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume	1

Conference

Conference	11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
Country/Territory	United States
City	Boca Raton, FL
Period	12/12/12 → 12/15/12

Keywords

Clustering
K-Means
interval computing

Access to Document

10.1109/ICMLA.2012.87

Cite this

Nordin, B., Hu, C., Chen, B., & Sheng, V. S. (2012). Interval-valued centroids in K-Means algorithms. In Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 (pp. 478-481). Article 6406669 (Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012; Vol. 1). https://doi.org/10.1109/ICMLA.2012.87

@inproceedings{6f0302bd0c15498ca9a559f9b92f6a84,

title = "Interval-valued centroids in K-Means algorithms",

abstract = "The K-Means algorithms are fundamental in machine learning and data mining. In this study, we investigate interval-valued rather than commonly used point-valued centroids in the K-Means algorithm. Using a proposed interval peak method to select initial interval centroids, we have obtained overall quality improvement of clusters on a set of test problems in the Fundamental Clustering Problem Suite (FCPS).",

keywords = "Clustering, K-Means, interval computing",

author = "Benjamine Nordin and Chenyi Hu and Bernard Chen and Sheng, {Victor S.}",

year = "2012",

doi = "10.1109/ICMLA.2012.87",

language = "English",

isbn = "9780769549132",

series = "Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012",

pages = "478--481",

booktitle = "Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012",

note = "11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 ; Conference date: 12-12-2012 Through 15-12-2012",

}

Nordin, B, Hu, C, Chen, B & Sheng, VS 2012, Interval-valued centroids in K-Means algorithms. in Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012., 6406669, Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, vol. 1, pp. 478-481, 11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012, Boca Raton, FL, United States, 12/12/12. https://doi.org/10.1109/ICMLA.2012.87

Interval-valued centroids in K-Means algorithms. / Nordin, Benjamine; Hu, Chenyi; Chen, Bernard et al.
Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012. 2012. p. 478-481 6406669 (Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012; Vol. 1).

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

TY - GEN

T1 - Interval-valued centroids in K-Means algorithms

AU - Nordin, Benjamine

AU - Hu, Chenyi

AU - Chen, Bernard

AU - Sheng, Victor S.

PY - 2012

Y1 - 2012

N2 - The K-Means algorithms are fundamental in machine learning and data mining. In this study, we investigate interval-valued rather than commonly used point-valued centroids in the K-Means algorithm. Using a proposed interval peak method to select initial interval centroids, we have obtained overall quality improvement of clusters on a set of test problems in the Fundamental Clustering Problem Suite (FCPS).

AB - The K-Means algorithms are fundamental in machine learning and data mining. In this study, we investigate interval-valued rather than commonly used point-valued centroids in the K-Means algorithm. Using a proposed interval peak method to select initial interval centroids, we have obtained overall quality improvement of clusters on a set of test problems in the Fundamental Clustering Problem Suite (FCPS).

KW - Clustering

KW - K-Means

KW - interval computing

UR - http://www.scopus.com/inward/record.url?scp=84873588274&partnerID=8YFLogxK

U2 - 10.1109/ICMLA.2012.87

DO - 10.1109/ICMLA.2012.87

M3 - Conference contribution

AN - SCOPUS:84873588274

SN - 9780769549132

T3 - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

SP - 478

EP - 481

BT - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

T2 - 11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012

Y2 - 12 December 2012 through 15 December 2012

ER -

Interval-valued centroids in K-Means algorithms

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this