Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors

Chunhua Ren; Linfu Sun; Qishi Wu

doi:10.1109/ISKE47853.2019.9170434

Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors

Chunhua Ren, Linfu Sun, Qishi Wu

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

3 Scopus citations

Abstract

Density Peaks Clustering is a novel clustering algorithm, which can find clusters of arbitrary shapes with fast speed. However, it has a few disadvantages, for example, when the data are unevenly distributed, the clustering performance is not good. Therefore, a improved DPC based on hierarchical k-nearest neighbor (HKNN-DPC) algorithm is proposed, which divided k-nearest neighbors into three layers, each layer of data points has different weight, and redesigned the local density calculation approach. We adopt the proposed algorithm to compare with DPC, DBSCAN and K-means in synthesized and UCI data sets. The experimental results indicated HKNN-DPC had better performance.

Original language	English (US)
Title of host publication	Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019
Editors	Li Zou, Lingling Fang, Bo Fu, Panpan Niu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	664-668
Number of pages	5
ISBN (Electronic)	9781728123486
DOIs	https://doi.org/10.1109/ISKE47853.2019.9170434
State	Published - Nov 2019
Externally published	Yes
Event	14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019 - Dalian, China Duration: Nov 14 2019 → Nov 16 2019

Publication series

Name	Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019

Conference

Conference	14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019
Country/Territory	China
City	Dalian
Period	11/14/19 → 11/16/19

All Science Journal Classification (ASJC) codes

Management Information Systems
Artificial Intelligence
Computational Theory and Mathematics
Computer Science Applications
Information Systems
Decision Sciences (miscellaneous)
Health Informatics

Keywords

density peaks clustering
hierarchical k-nearest neighbors
local density
uneven density distribution

Access to Document

10.1109/ISKE47853.2019.9170434

Cite this

Ren, C., Sun, L., & Wu, Q. (2019). Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors. In L. Zou, L. Fang, B. Fu, & P. Niu (Eds.), Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019 (pp. 664-668). Article 9170434 (Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISKE47853.2019.9170434

Ren, Chunhua ; Sun, Linfu ; Wu, Qishi. / Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors. Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019. editor / Li Zou ; Lingling Fang ; Bo Fu ; Panpan Niu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 664-668 (Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019).

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title = "Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors",

abstract = "Density Peaks Clustering is a novel clustering algorithm, which can find clusters of arbitrary shapes with fast speed. However, it has a few disadvantages, for example, when the data are unevenly distributed, the clustering performance is not good. Therefore, a improved DPC based on hierarchical k-nearest neighbor (HKNN-DPC) algorithm is proposed, which divided k-nearest neighbors into three layers, each layer of data points has different weight, and redesigned the local density calculation approach. We adopt the proposed algorithm to compare with DPC, DBSCAN and K-means in synthesized and UCI data sets. The experimental results indicated HKNN-DPC had better performance.",

keywords = "density peaks clustering, hierarchical k-nearest neighbors, local density, uneven density distribution",

author = "Chunhua Ren and Linfu Sun and Qishi Wu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019 ; Conference date: 14-11-2019 Through 16-11-2019",

year = "2019",

month = nov,

doi = "10.1109/ISKE47853.2019.9170434",

language = "English (US)",

series = "Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019",

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

pages = "664--668",

editor = "Li Zou and Lingling Fang and Bo Fu and Panpan Niu",

booktitle = "Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019",

address = "United States",

}

Ren, C, Sun, L & Wu, Q 2019, Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors. in L Zou, L Fang, B Fu & P Niu (eds), Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019., 9170434, Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019, Institute of Electrical and Electronics Engineers Inc., pp. 664-668, 14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019, Dalian, China, 11/14/19. https://doi.org/10.1109/ISKE47853.2019.9170434

Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors. / Ren, Chunhua; Sun, Linfu; Wu, Qishi.
Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019. ed. / Li Zou; Lingling Fang; Bo Fu; Panpan Niu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 664-668 9170434 (Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019).

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

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T1 - Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors

AU - Ren, Chunhua

AU - Sun, Linfu

AU - Wu, Qishi

PY - 2019/11

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N2 - Density Peaks Clustering is a novel clustering algorithm, which can find clusters of arbitrary shapes with fast speed. However, it has a few disadvantages, for example, when the data are unevenly distributed, the clustering performance is not good. Therefore, a improved DPC based on hierarchical k-nearest neighbor (HKNN-DPC) algorithm is proposed, which divided k-nearest neighbors into three layers, each layer of data points has different weight, and redesigned the local density calculation approach. We adopt the proposed algorithm to compare with DPC, DBSCAN and K-means in synthesized and UCI data sets. The experimental results indicated HKNN-DPC had better performance.

AB - Density Peaks Clustering is a novel clustering algorithm, which can find clusters of arbitrary shapes with fast speed. However, it has a few disadvantages, for example, when the data are unevenly distributed, the clustering performance is not good. Therefore, a improved DPC based on hierarchical k-nearest neighbor (HKNN-DPC) algorithm is proposed, which divided k-nearest neighbors into three layers, each layer of data points has different weight, and redesigned the local density calculation approach. We adopt the proposed algorithm to compare with DPC, DBSCAN and K-means in synthesized and UCI data sets. The experimental results indicated HKNN-DPC had better performance.

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KW - hierarchical k-nearest neighbors

KW - local density

KW - uneven density distribution

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Ren C, Sun L, Wu Q. Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors. In Zou L, Fang L, Fu B, Niu P, editors, Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 664-668. 9170434. (Proceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019). doi: 10.1109/ISKE47853.2019.9170434

Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors

Abstract

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All Science Journal Classification (ASJC) codes

Keywords

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