A weight-incorporated similarity-based clustering ensemble method

Shi Yao Liu; Qi Kang; Jing An; Mengchu Zhou

doi:10.1109/ICNSC.2014.6819714

A weight-incorporated similarity-based clustering ensemble method

Shi Yao Liu, Qi Kang, Jing An, Mengchu Zhou

Electrical and Computer Engineering

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

10 Scopus citations

Abstract

Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can be effectively improved. This work investigates similarity-based methods and proposes a new method called weight- incorporated similarity-based clustering ensemble (WSCE). Six classic data sets are used to test single clustering algorithms, similarity-based one, and the proposed one via simulation. The results prove the validity and performance advantage of the proposed method.

Original language	English (US)
Title of host publication	Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
Publisher	IEEE Computer Society
Pages	719-724
Number of pages	6
ISBN (Print)	9781479931064
DOIs	https://doi.org/10.1109/ICNSC.2014.6819714
State	Published - Jan 1 2014
Event	11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 - Miami, FL, United States Duration: Apr 7 2014 → Apr 9 2014

Other

Other	11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
Country/Territory	United States
City	Miami, FL
Period	4/7/14 → 4/9/14

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Control and Systems Engineering

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10.1109/ICNSC.2014.6819714

Cite this

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title = "A weight-incorporated similarity-based clustering ensemble method",

abstract = "Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can be effectively improved. This work investigates similarity-based methods and proposes a new method called weight- incorporated similarity-based clustering ensemble (WSCE). Six classic data sets are used to test single clustering algorithms, similarity-based one, and the proposed one via simulation. The results prove the validity and performance advantage of the proposed method.",

author = "Liu, {Shi Yao} and Qi Kang and Jing An and Mengchu Zhou",

year = "2014",

month = jan,

day = "1",

doi = "10.1109/ICNSC.2014.6819714",

language = "English (US)",

isbn = "9781479931064",

pages = "719--724",

booktitle = "Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014",

publisher = "IEEE Computer Society",

address = "United States",

note = "11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 ; Conference date: 07-04-2014 Through 09-04-2014",

}

Liu, SY, Kang, Q, An, J & Zhou, M 2014, A weight-incorporated similarity-based clustering ensemble method. in Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014., 6819714, IEEE Computer Society, pp. 719-724, 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014, Miami, FL, United States, 4/7/14. https://doi.org/10.1109/ICNSC.2014.6819714

TY - GEN

T1 - A weight-incorporated similarity-based clustering ensemble method

AU - Liu, Shi Yao

AU - Kang, Qi

AU - An, Jing

AU - Zhou, Mengchu

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can be effectively improved. This work investigates similarity-based methods and proposes a new method called weight- incorporated similarity-based clustering ensemble (WSCE). Six classic data sets are used to test single clustering algorithms, similarity-based one, and the proposed one via simulation. The results prove the validity and performance advantage of the proposed method.

AB - Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can be effectively improved. This work investigates similarity-based methods and proposes a new method called weight- incorporated similarity-based clustering ensemble (WSCE). Six classic data sets are used to test single clustering algorithms, similarity-based one, and the proposed one via simulation. The results prove the validity and performance advantage of the proposed method.

UR - http://www.scopus.com/inward/record.url?scp=84902376067&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902376067&partnerID=8YFLogxK

U2 - 10.1109/ICNSC.2014.6819714

DO - 10.1109/ICNSC.2014.6819714

M3 - Conference contribution

AN - SCOPUS:84902376067

SN - 9781479931064

SP - 719

EP - 724

BT - Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014

PB - IEEE Computer Society

T2 - 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014

Y2 - 7 April 2014 through 9 April 2014

ER -

A weight-incorporated similarity-based clustering ensemble method

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