Swarm Clustering Algorithm: Let the Particles Fly for a while

Wenjie Zhu; Wenjian Luo; Li Ni; Nannan Lu

doi:10.1109/SSCI.2018.8628884

Swarm Clustering Algorithm: Let the Particles Fly for a while

Wenjie Zhu
, Wenjian Luo^*
, Li Ni
, Nannan Lu

^*Corresponding author for this work

University of Science and Technology of China

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

8 Scopus citations

Abstract

Swarm Intelligence (SI) and Evolutionary Algorithms (EAs) have been widely used for cluster analysis of spatial data. However, in most existing SI, particles are encoded to represent the centers of clusters. In this paper, inspired by Particle Swarm optimization (PSO), a novel Swarm Clustering Algorithm (SCA) is proposed, which has the potential ability to deal with the data of the arbitrary number, shape and size of clusters. In SCA, a particle is a point in the dataset under cluster analysis. Thus, the number of particles in the swarm is equal to the size of the dataset. All particles interact dynamically with similar particles, and fly to the denser areas to form clusters. The experimental results show that our algorithm is effective.

Original language	English
Title of host publication	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Editors	Suresh Sundaram
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1242-1249
Number of pages	8
ISBN (Electronic)	9781538692769
DOIs	https://doi.org/10.1109/SSCI.2018.8628884
State	Published - 2 Jul 2018
Externally published	Yes
Event	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India Duration: 18 Nov 2018 → 21 Nov 2018

Publication series

Name	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

Conference

Conference	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Country/Territory	India
City	Bangalore
Period	18/11/18 → 21/11/18

Keywords

Clustering
Evolutionary Algorithms
Kernel Density Estimation
Particle Swarm optimization
Swarm Intelligence

Access to Document

10.1109/SSCI.2018.8628884

Cite this

Zhu, W., Luo, W., Ni, L., & Lu, N. (2018). Swarm Clustering Algorithm: Let the Particles Fly for a while. In S. Sundaram (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 1242-1249). Article 8628884 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2018.8628884

Zhu, Wenjie ; Luo, Wenjian ; Ni, Li et al. / Swarm Clustering Algorithm : Let the Particles Fly for a while. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. editor / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1242-1249 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

@inproceedings{505d768ddb384998a29b2a0851274b8b,

title = "Swarm Clustering Algorithm: Let the Particles Fly for a while",

abstract = "Swarm Intelligence (SI) and Evolutionary Algorithms (EAs) have been widely used for cluster analysis of spatial data. However, in most existing SI, particles are encoded to represent the centers of clusters. In this paper, inspired by Particle Swarm optimization (PSO), a novel Swarm Clustering Algorithm (SCA) is proposed, which has the potential ability to deal with the data of the arbitrary number, shape and size of clusters. In SCA, a particle is a point in the dataset under cluster analysis. Thus, the number of particles in the swarm is equal to the size of the dataset. All particles interact dynamically with similar particles, and fly to the denser areas to form clusters. The experimental results show that our algorithm is effective.",

keywords = "Clustering, Evolutionary Algorithms, Kernel Density Estimation, Particle Swarm optimization, Swarm Intelligence",

author = "Wenjie Zhu and Wenjian Luo and Li Ni and Nannan Lu",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 ; Conference date: 18-11-2018 Through 21-11-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/SSCI.2018.8628884",

language = "英语",

series = "Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018",

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

pages = "1242--1249",

editor = "Suresh Sundaram",

booktitle = "Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018",

address = "美国",

}

Zhu, W, Luo, W, Ni, L & Lu, N 2018, Swarm Clustering Algorithm: Let the Particles Fly for a while. in S Sundaram (ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018., 8628884, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1242-1249, 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 18/11/18. https://doi.org/10.1109/SSCI.2018.8628884

Swarm Clustering Algorithm: Let the Particles Fly for a while. / Zhu, Wenjie; Luo, Wenjian; Ni, Li et al.
Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. ed. / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1242-1249 8628884 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

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

TY - GEN

T1 - Swarm Clustering Algorithm

T2 - 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018

AU - Zhu, Wenjie

AU - Luo, Wenjian

AU - Ni, Li

AU - Lu, Nannan

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Swarm Intelligence (SI) and Evolutionary Algorithms (EAs) have been widely used for cluster analysis of spatial data. However, in most existing SI, particles are encoded to represent the centers of clusters. In this paper, inspired by Particle Swarm optimization (PSO), a novel Swarm Clustering Algorithm (SCA) is proposed, which has the potential ability to deal with the data of the arbitrary number, shape and size of clusters. In SCA, a particle is a point in the dataset under cluster analysis. Thus, the number of particles in the swarm is equal to the size of the dataset. All particles interact dynamically with similar particles, and fly to the denser areas to form clusters. The experimental results show that our algorithm is effective.

AB - Swarm Intelligence (SI) and Evolutionary Algorithms (EAs) have been widely used for cluster analysis of spatial data. However, in most existing SI, particles are encoded to represent the centers of clusters. In this paper, inspired by Particle Swarm optimization (PSO), a novel Swarm Clustering Algorithm (SCA) is proposed, which has the potential ability to deal with the data of the arbitrary number, shape and size of clusters. In SCA, a particle is a point in the dataset under cluster analysis. Thus, the number of particles in the swarm is equal to the size of the dataset. All particles interact dynamically with similar particles, and fly to the denser areas to form clusters. The experimental results show that our algorithm is effective.

KW - Clustering

KW - Evolutionary Algorithms

KW - Kernel Density Estimation

KW - Particle Swarm optimization

KW - Swarm Intelligence

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DO - 10.1109/SSCI.2018.8628884

M3 - 会议稿件

AN - SCOPUS:85062800152

T3 - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

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BT - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

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Zhu W, Luo W, Ni L, Lu N. Swarm Clustering Algorithm: Let the Particles Fly for a while. In Sundaram S, editor, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1242-1249. 8628884. (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). doi: 10.1109/SSCI.2018.8628884

Swarm Clustering Algorithm: Let the Particles Fly for a while

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