A Weighted K-means Algorithm Based on Differential Evolution

Fengling Wang

doi:10.1109/IMCEC.2018.8469472

A Weighted K-means Algorithm Based on Differential Evolution

Fengling Wang^*

^*Corresponding author for this work

Hezhou University

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

2 Scopus citations

Abstract

The k-means algorithm is constrained by the initial clustering centers and abnormal data, and unstable clustering results are easy to occur. In order to solve this problem, this dissertation firstly analyzes the research status of the differential evolution algorithm, introduces the basic idea and advantages of the differential evolution algorithm, proposes a weighted evolution algorithm based on differential evolution, adopts the differential evolution algorithm with strong global search ability, Initial clustering centers. According to the different influence degree of samples on clustering analysis, weights are introduced to design a weighted Euclidean distance to reduce the adverse effects of uncertainties such as outliers and to obtain a stable clustering result The experimental results show that the initial clustering center selected by the algorithm is closer to the final clustering center, and the computational efficiency of the algorithm is improved while ensuring the clustering accuracy.

Original language	English
Title of host publication	Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2269-2274
Number of pages	6
ISBN (Electronic)	9781538618035
DOIs	https://doi.org/10.1109/IMCEC.2018.8469472
State	Published - 20 Sep 2018
Externally published	Yes
Event	2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018 - Xi'an, China Duration: 25 May 2018 → 27 May 2018

Publication series

Name	Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

Conference

Conference	2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
Country/Territory	China
City	Xi'an
Period	25/05/18 → 27/05/18

Keywords

clustering
differential evolution
k-means algorithm

Access to Document

10.1109/IMCEC.2018.8469472

Cite this

Wang, F. (2018). A Weighted K-means Algorithm Based on Differential Evolution. In B. Xu (Ed.), Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018 (pp. 2269-2274). Article 8469472 (Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IMCEC.2018.8469472

Wang, Fengling. / A Weighted K-means Algorithm Based on Differential Evolution. Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2269-2274 (Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018).

@inproceedings{cccb6e67ad854c79a02a2981da4ee1d4,

title = "A Weighted K-means Algorithm Based on Differential Evolution",

abstract = "The k-means algorithm is constrained by the initial clustering centers and abnormal data, and unstable clustering results are easy to occur. In order to solve this problem, this dissertation firstly analyzes the research status of the differential evolution algorithm, introduces the basic idea and advantages of the differential evolution algorithm, proposes a weighted evolution algorithm based on differential evolution, adopts the differential evolution algorithm with strong global search ability, Initial clustering centers. According to the different influence degree of samples on clustering analysis, weights are introduced to design a weighted Euclidean distance to reduce the adverse effects of uncertainties such as outliers and to obtain a stable clustering result The experimental results show that the initial clustering center selected by the algorithm is closer to the final clustering center, and the computational efficiency of the algorithm is improved while ensuring the clustering accuracy.",

keywords = "clustering, differential evolution, k-means algorithm",

author = "Fengling Wang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018 ; Conference date: 25-05-2018 Through 27-05-2018",

year = "2018",

month = sep,

day = "20",

doi = "10.1109/IMCEC.2018.8469472",

language = "英语",

series = "Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2269--2274",

editor = "Bing Xu",

booktitle = "Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018",

address = "美国",

}

Wang, F 2018, A Weighted K-means Algorithm Based on Differential Evolution. in B Xu (ed.), Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018., 8469472, Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2269-2274, 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, Xi'an, China, 25/05/18. https://doi.org/10.1109/IMCEC.2018.8469472

A Weighted K-means Algorithm Based on Differential Evolution. / Wang, Fengling.
Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2269-2274 8469472 (Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018).

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

TY - GEN

T1 - A Weighted K-means Algorithm Based on Differential Evolution

AU - Wang, Fengling

PY - 2018/9/20

Y1 - 2018/9/20

N2 - The k-means algorithm is constrained by the initial clustering centers and abnormal data, and unstable clustering results are easy to occur. In order to solve this problem, this dissertation firstly analyzes the research status of the differential evolution algorithm, introduces the basic idea and advantages of the differential evolution algorithm, proposes a weighted evolution algorithm based on differential evolution, adopts the differential evolution algorithm with strong global search ability, Initial clustering centers. According to the different influence degree of samples on clustering analysis, weights are introduced to design a weighted Euclidean distance to reduce the adverse effects of uncertainties such as outliers and to obtain a stable clustering result The experimental results show that the initial clustering center selected by the algorithm is closer to the final clustering center, and the computational efficiency of the algorithm is improved while ensuring the clustering accuracy.

AB - The k-means algorithm is constrained by the initial clustering centers and abnormal data, and unstable clustering results are easy to occur. In order to solve this problem, this dissertation firstly analyzes the research status of the differential evolution algorithm, introduces the basic idea and advantages of the differential evolution algorithm, proposes a weighted evolution algorithm based on differential evolution, adopts the differential evolution algorithm with strong global search ability, Initial clustering centers. According to the different influence degree of samples on clustering analysis, weights are introduced to design a weighted Euclidean distance to reduce the adverse effects of uncertainties such as outliers and to obtain a stable clustering result The experimental results show that the initial clustering center selected by the algorithm is closer to the final clustering center, and the computational efficiency of the algorithm is improved while ensuring the clustering accuracy.

KW - clustering

KW - differential evolution

KW - k-means algorithm

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U2 - 10.1109/IMCEC.2018.8469472

DO - 10.1109/IMCEC.2018.8469472

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BT - Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

A2 - Xu, Bing

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 25 May 2018 through 27 May 2018

ER -

Wang F. A Weighted K-means Algorithm Based on Differential Evolution. In Xu B, editor, Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2269-2274. 8469472. (Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018). doi: 10.1109/IMCEC.2018.8469472

A Weighted K-means Algorithm Based on Differential Evolution

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