research-article

Stratified multi-density spectral clustering using Gaussian mixture model

Authors:

Xueying WangAuthors Info & Claims

Volume 633, Issue C

Pages 182 - 203

https://doi.org/10.1016/j.ins.2023.03.067

Published: 01 July 2023 Publication History

Abstract

Spectral clustering aims to minimise inter-cluster similarity by constructing graph model, which possesses a significant effect in data of arbitrary shape. Nonetheless, there are still two limitations in the existing algorithms. First, spectral methods perform poorly if the densities of instances vary greatly. Second, they are challenging to handle the complex distribution that different objects of densities are mixed. To address the two limitations, in this paper, a novel spectral clustering algorithm is constructed to accommodate more complex multi-density data. The idea of density stratification is proposed through the probability density of Gaussian mixture model, and the number of layers can be automatically determined by the defined scatter index. Then, a new density ratio is established to effectively reduce the density imbalance of diverse instances based on mutual nearest-neighbour and density stratification, which is more advantageous in the clusters with less clear boundaries. Finally, the optimised adjacency matrix and multi-density spectral clustering algorithm are induced to improve the effect of multi-density data. As per experimental results, the proposed algorithm generally outperforms the popular representative algorithms for both synthetic and benchmark datasets, and works more effectively on multi-density as well as single-density data, which illustrates the superiority of the proposed algorithm.

References

[1]

S. El Hajjar, F. Dornaika, F. Abdallah, One-step multi-view spectral clustering with cluster label correlation graph, Inf. Sci. 592 (2022) 97–111.

Digital Library

[2]

S. Ding, W. Du, X. Xu, T. Shi, Y. Wang, C. Li, An improved density peaks clustering algorithm based on natural neighbor with a merging strategy, Inf. Sci. 624 (2023) 252–276.

[3]

Y. Wang, W. Pang, Z. Jiao, An adaptive mutual k-nearest neighbors clustering algorithm based on maximizing mutual information, Pattern Recognit. 137 (2023).

[4]

Y. Wang, W. Pang, J. Zhou, An improved density peak clustering algorithm guided by pseudo labels, Knowl.-Based Syst. 252 (2022).

[5]

J. Wang, H. Wang, Z. Ma, L. Wang, Q. Wang, X. Li, Unsupervised hyperspectral band selection based on hypergraph spectral clustering, IEEE Geosci. Remote Sens. Lett. 19 (2021) 1–5.

[6]

W.J. Farmer, A.J. Rix, Evaluating power system network inertia using spectral clustering to define local area stability, Int. J. Electr. Power Energy Syst. 134 (2022).

[7]

H. Jia, L. Wang, H. Song, Q. Mao, S. Ding, An efficient Nyström spectral clustering algorithm using incomplete Cholesky decomposition, Expert Syst. Appl. 186 (2021).

[8]

MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, no. 14, 1967, pp. 281–297.

[9]

Q. Wang, Z. Qin, F. Nie, X. Li, Spectral embedded adaptive neighbors clustering, IEEE Trans. Neural Netw. Learn. Syst. 30 (4) (2018) 1265–1271.

[10]

A. Monney, Y. Zhan, Z. Jiang, B.-B. Benuwa, A multi-kernel method of measuring adaptive similarity for spectral clustering, Expert Syst. Appl. 159 (2020).

[11]

M. Alshammari, M. Takatsuka, Approximate spectral clustering with eigenvector selection and self-tuned k, Pattern Recognit. Lett. 122 (2019) 31–37.

[12]

M. Zamiri, T. Bahraini, H.S. Yazdi, Mvdf-rsc: multi-view data fusion via robust spectral clustering for geo-tagged image tagging, Expert Syst. Appl. 173 (2021).

Digital Library

[13]

Y. Peng, X. Zhu, F. Nie, W. Kong, Y. Ge, Fuzzy graph clustering, Inf. Sci. 571 (2021) 38–49.

[14]

Z. Kang, C. Peng, Q. Cheng, Z. Xu, Unified spectral clustering with optimal graph, Proc. AAAI Conf. Artif. Intell. 32 (1) (2018) 3366–3373.

[15]

D. Huang, C.-D. Wang, J.-S. Wu, J.-H. Lai, C.-K. Kwoh, Ultra-scalable spectral clustering and ensemble clustering, IEEE Trans. Knowl. Data Eng. 32 (6) (2019) 1212–1226.

[16]

Y. Nataliani, M.-S. Yang, Powered Gaussian kernel spectral clustering, Neural Comput. Appl. 31 (1) (2019) 557–572.

[17]

X. Hu, H. Zhang, C. Yang, X. Zhao, B. Li, Regularized spectral clustering with entropy perturbation, IEEE Trans. Big Data 7 (6) (2020) 967–972.

[18]

M. Hosseini, F.T. Azar, A new eigenvector selection strategy applied to develop spectral clustering, Multidimens. Syst. Signal Process. 28 (4) (2017) 1227–1248.

[19]

G. Zhong, C.-M. Pun, Self-taught multi-view spectral clustering, Pattern Recognit. (2023).

[20]

M. Zamiri, H.S. Yazdi, Image annotation based on multi-view robust spectral clustering, J. Vis. Commun. Image Represent. 74 (2021).

[21]

Y. Zhao, Y. Yuan, F. Nie, Q. Wang, Spectral clustering based on iterative optimization for large-scale and high-dimensional data, Neurocomputing 318 (2018) 227–235.

[22]

X. Cai, D. Huang, C.-D. Wang, C.-K. Kwoh, Spectral clustering by subspace randomization and graph fusion for high-dimensional data, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2020, pp. 330–342.

[23]

G. Yang, S. Deng, X. Chen, C. Chen, Y. Yang, Z. Gong, Z. Hao, Reskm: a general framework to accelerate large-scale spectral clustering, Pattern Recognit. 137 (2023).

[24]

D. Liu, J. Li, Pgcas: a parallelized graph clustering algorithm based on spark, in: International Conference on Big Scientific Data Management, Springer, 2018, pp. 186–198.

[25]

Y. Jia, S. Kwong, J. Hou, Semi-supervised spectral clustering with structured sparsity regularization, IEEE Signal Process. Lett. 25 (3) (2018) 403–407.

[26]

M.A. Masud, J.Z. Huang, C. Wei, J. Wang, I. Khan, M. Zhong, I-nice: a new approach for identifying the number of clusters and initial cluster centres, Inf. Sci. 466 (2018) 129–151.

[27]

X. Chen, W. Hong, F. Nie, D. He, M. Yang, J.Z. Huang, Spectral clustering of large-scale data by directly solving normalized cut, in: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 1206–1215.

[28]

X. Yang, C. Deng, F. Zheng, J. Yan, W. Liu, Deep spectral clustering using dual autoencoder network, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4066–4075.

[29]

X. Zhu, S. Zhang, W. He, R. Hu, C. Lei, P. Zhu, One-step multi-view spectral clustering, IEEE Trans. Knowl. Data Eng. 31 (10) (2018) 2022–2034.

[30]

T. Cheng, An improved dbscan clustering algorithm for multi-density datasets, in: Proceedings of the 2nd International Conference on Intelligent Information Processing, 2017, pp. 1–5.

[31]

A.Z. Khan, S.U. Rehman, H. Israr, K. Aziz, An enhanced multi density based clustering technique using density level partition (edscan-dlp), J. Sci. Res. 120 (2) (2020).

[32]

J. Xie, Z.-Y. Xiong, Y.-F. Zhang, Y. Feng, J. Ma, Density core-based clustering algorithm with dynamic scanning radius, Knowl.-Based Syst. 142 (2018) 58–70.

Digital Library

[33]

M. Afzalan, F. Jazizadeh, An automated spectral clustering for multi-scale data, Neurocomputing 347 (2019) 94–108.

[34]

C. Mari, C. Baldassari, Unsupervised expectation-maximization algorithm initialization for mixture models: a complex network-driven approach for modeling financial time series, Inf. Sci. 617 (2022) 1–16.

[35]

E. Azhir, N.J. Navimipour, M. Hosseinzadeh, A. Sharifi, A. Darwesh, An automatic clustering technique for query plan recommendation, Inf. Sci. 545 (2021) 620–632.

[36]

S.M. Taghavi-Shahri, A. Fassò, B. Mahaki, H. Amini, Concurrent spatiotemporal daily land use regression modeling and missing data imputation of fine particulate matter using distributed space-time expectation maximization, Atmos. Environ. 224 (2020).

[37]

Y. Chen, L. Zhou, N. Bouguila, C. Wang, Y. Chen, J. Du, Block-dbscan: fast clustering for large scale data, Pattern Recognit. 109 (2021).

Digital Library

[38]

L. Zelnik-Manor, P. Perona, Self-tuning spectral clustering, Adv. Neural Inf. Process. Syst. 17 (2004).

[39]

X. Zhang, J. Li, H. Yu, Local density adaptive similarity measurement for spectral clustering, Pattern Recognit. Lett. 32 (2) (2011) 352–358.

[40]

K.M. Kumar, A.R.M. Reddy, A fast dbscan clustering algorithm by accelerating neighbor searching using groups method, Pattern Recognit. 58 (2016) 39–48.

[41]

A. Rodriguez, A. Laio, Clustering by fast search and find of density peaks, Science 344 (6191) (2014) 1492–1496.

[42]

Y. Wang, D. Wang, Y. Zhou, X. Zhang, C. Quek, Vdpc: variational density peak clustering algorithm, Inf. Sci. 621 (2023) 627–651.

[43]

W. Guo, W. Wang, S. Zhao, Y. Niu, Z. Zhang, X. Liu, Density peak clustering with connectivity estimation, Knowl.-Based Syst. 243 (2022).

Digital Library

[44]

H. Liu, J. Wu, T. Liu, D. Tao, Y. Fu, Spectral ensemble clustering via weighted k-means: theoretical and practical evidence, IEEE Trans. Knowl. Data Eng. 29 (5) (2017) 1129–1143.

Digital Library

[45]

D. Huang, C.-D. Wang, J.-H. Lai, Locally weighted ensemble clustering, IEEE Trans. Cybern. 48 (5) (2017) 1460–1473.

[46]

D. Huang, C.-D. Wang, H. Peng, J. Lai, C.-K. Kwoh, Enhanced ensemble clustering via fast propagation of cluster-wise similarities, IEEE Trans. Syst. Man Cybern. Syst. 51 (1) (2021) 508–520.

[47]

Y. Qu, Q. Fu, C. Shang, A. Deng, R. Zwiggelaar, M. George, Q. Shen, Fuzzy-rough assisted refinement of image processing procedure for mammographic risk assessment, Appl. Soft Comput. 91 (2020).

[48]

Y. Qu, G. Yue, C. Shang, L. Yang, R. Zwiggelaar, Q. Shen, Multi-criterion mammographic risk analysis supported with multi-label fuzzy-rough feature selection, Artif. Intell. Med. 100 (2019).

Cited By

Li YChen SLang G(2024)Fuzzy Granular-Balls Based Spectral ClusteringRough Sets10.1007/978-3-031-65665-1_16(252-265)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-65665-1_16
Shi YWang YZhao Z(2023)The Density-graded Clustering AlgorithmProceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering10.1145/3650400.3650587(1108-1113)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3650400.3650587

Recommendations

Density Peaks Clustering Algorithm Based on Density Stratification and Subcluster Fusion
ACAI '22: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

In the density peaks clustering algorithm (DPC), points in high density regions are more likely to be selected as cluster centers, resulting in poor performance on complex datasets that are of various densities. A Density Peaks Clustering Algorithm ...
PARAFAC-Based Blind Identification of Underdetermined Mixtures Using Gaussian Mixture Model

This paper presents a novel algorithm, named GMM-PARAFAC, for blind identification of underdetermined instantaneous linear mixtures. The GMM-PARAFAC algorithm uses Gaussian mixture model (GMM) to model non-Gaussianity of the independent sources. We show ...
A robust EM clustering algorithm for Gaussian mixture models

Clustering is a useful tool for finding structure in a data set. The mixture likelihood approach to clustering is a popular clustering method, in which the EM algorithm is the most used method. However, the EM algorithm for Gaussian mixture models is ...

Comments

Information & Contributors

Information

Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 633, Issue C

Jul 2023

633 pages

ISSN:0020-0255

Issue’s Table of Contents

Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 July 2023

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li YChen SLang G(2024)Fuzzy Granular-Balls Based Spectral ClusteringRough Sets10.1007/978-3-031-65665-1_16(252-265)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-65665-1_16
Shi YWang YZhao Z(2023)The Density-graded Clustering AlgorithmProceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering10.1145/3650400.3650587(1108-1113)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3650400.3650587

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents