research-article

An improved weighted ensemble clustering based on two-tier uncertainty measurement

Authors:

Di LiuAuthors Info & Claims

Volume 238, Issue PA

https://doi.org/10.1016/j.eswa.2023.121672

Published: 27 February 2024 Publication History

Abstract

Existing locally weighted ensemble clustering algorithms strive to weight each cluster and take into account the differences among all clusters, but they tend to ignore the basic cluster labels. The purpose of this paper is to combine the influence of cluster level and the base clustering level in a unified ensemble clustering framework. A novel two-level weighted ensemble cluster method (TWEC) is proposed, which inserts a global weighting strategy into a local ensemble cluster learning framework. First, the cluster uncertainty based on an entropy criterion is refined by considering the base clustering labels for each cluster. Then, the two-level uncertainty is converted to cluster reliability via improved ensemble-driven cluster validity measure. Finally, two novel consensus functions are developed. Experiments validate the effectiveness of the proposed TWEC framework by comparing it with ten comparison algorithms on fourteen real-world datasets and twenty synthetic datasets. The results show that TWEC framework can improve the robustness and stability of clustering.

References

[1]

A. Banerjee, A.K. Pujari, C.R. Panigrahi, B. Pati, S.C. Nayak, …., T.H. Weng, A new method for weighted ensemble clustering and coupled ensemble selection, CONNECTION SCIENCE 33 (3) (2021) 623–644,.

[2]

V.B. Berikov, Construction of an optimal collective decision in cluster analysis on the basis of an averaged co-association matrix and cluster validity indices, Pattern Recognition and Image Analysis 27 (2) (2017) 153–165,.

Digital Library

[3]

M.U. Chowdhury, J.H. Abawajy, A. Kelarev, H.F. Jelinek, A Clustering-Based Multi-Layer Distributed Ensemble for Neurological Diagnostics in Cloud Services, IEEE TRANSACTIONS ON CLOUD COMPUTING 8 (2) (2020) 473–483,.

[4]

Du, H. Y., Wang, W. J., Bai, L., & Feng, J. S. (2022). A Generative Clustering Ensemble Model and Its Application in IoT Data Analysis. WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022.

[5]

W.W. Fang, X.N. Yao, X.J. Zhao, J.W. Yin, N.X. Xiong, A Stochastic Control Approach to Maximize Profit on Service Provisioning for Mobile Cloudlet Platforms, IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 48 (4) (2018) 522–534,.

[6]

Fern, X. Z., & Brodley, C. E. (2003). Random projection for high dimensional data clustering: a cluster ensemble approach. in Machine Learning, Proceedings of the Twentieth International Conference (ICML).

[7]

Fern, X.Z., & C.E. Brodley.(2004). Solving cluster ensemble problems by bipartite graph partitioning. in Proceedings of the twenty-first international conference on Machine learning. https://doi.org/110.1145/1015330.1015414.

[8]

A.L.N. Fred, A.K. Jain, Combining multiple clusterings using evidence accumulation, IEEE transactions on pattern analysis and machine intelligence 27 (6) (2005) 835–850,.

Digital Library

[9]

C. Gao, W. Pedrycz, D. Miao, Rough subspace-based clustering ensemble for categorical data, Soft Computing 17 (9) (2013) 1643–1658.

[10]

K. Golalipour, E. Akbari, S.S. Hamidi, M. Lee, R. Enayatifar, From clustering to clustering ensemble selection: A review, Engineering Applications of Artificial Intelligence 104 (2021),.

[11]

R. Guan, H. Zhang, Y. Liang, F. Giunchiglia, X. Feng, Deep feature-based text clustering and its explanation, IEEE Transactions on Knowledge and Data Engineering PP(99) (2020) 1,.

[12]

S.T. Hadjitodorov, L.I. Kuncheva, L.P. Todorova, Moderate diversity for better cluster ensemble, Information Fusion 7 (3) (2005) 264–275,.

Digital Library

[13]

D. Huang C. Wang J. Lai LWMC: A Locally Weighted Meta-Clustering Algorithm for Ensemble Clustering 2017 Springer International Publishing in Cham 10.1007/978-3-319-70139-4_17.

[14]

D. Huang, J. Lai, C. Wang, Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis, Neurocomputing 170 (2015) 240–250,.

Digital Library

[15]

D. Huang, C.D. Wang, J.H. Lai, Locally Weighted Ensemble Clustering, IEEE TRANSACTIONS ON CYBERNETICS 48 (5) (2018) 1460–1473,.

[16]

J., W., H., L., H., X., J., C., & J., C. (2015). K-Means-Based Consensus Clustering: A Unified View. IEEE Transactions on Knowledge and Data Engineering, 27(1), 155-169. https://doi.org/10.1109/TKDE.2014.2316512.

[17]

A.K. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters 31 (8) (2010) 651–666,.

Digital Library

[18]

R. Jothi, S.K. Mohanty, A. Ojha, Gene expression clustering using local neighborhood-based similarity measures, Computers & Electrical Engineering 91 (11) (2021),.

[19]

Y.X. Lai, S.Y. He, Z.J. Lin, F. Yang, Q. Zhou, …., F.,... Zhou, X. F., An Adaptive Robust Semi-Supervised Clustering Framework Using Weighted Consensus of Random k-Means Ensemble, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 33 (5) (2021) 1877–1890,.

[20]

W. Li, Z. Wang, W. Sun, S. Bahrami, An Ensemble Clustering Framework Based on Hierarchical Clustering Ensemble Selection and Clusters Clustering, Cybernetics and Systems 1–26 (2022),.

[21]

H.F. Liu, J.J. Wu, T.L. Liu, D.H. Tao, Y. Fu, Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 29 (5) (2017) 1129–1143,.

Digital Library

[22]

W. Shao, S.J. Huang, M.X. Liu, D.Q. Zhang, Querying Representative and Informative Super-pixels for Filament Segmentation in Bioimages, IEEE/ACM Transactions on Computational Biology and Bioinformatics 17 (4) (2020) 1394–1405,.

Digital Library

[23]

A. Strehl, J. Ghosh, Cluster ensembles –- a knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research 3 (3) (2003) 583–617,.

Digital Library

[24]

Tao, Z.T., Liu, H.F., Li, s., Ding, Z. M., & Fu, Y. (2017). From Ensemble Clustering to Multi-View Clustering. in the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). https://doi.org/10.24963/ijcai.2017/396.

[25]

A.P. Topchy, A.K. Jain, W.F. Punch, Clustering ensembles: Models of consensus and weak partitions, IEEE Transactions on Pattern Analysis & Machine Intelligence 27 (12) (2005) 1866–1881,.

Digital Library

[26]

N.X. Vinh, J. Epps, J. Bailey, Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance, Journal of Machine Learning Research 11 (2010) 2837–2854,.

Digital Library

[27]

Wang, L., & Zhang, G. (2015). Cluster Ensemble Based Image Segmentation Algorithm. in Proceedings of the 2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE). https://doi.org/10.5772/56769.

[28]

M. Wang, X.L. Zhang, S. Rahardja, An unsupervised deep learning system for acoustic scene analysis, Applied Sciences 10 (6) (2020) 2076,.

[29]

X.G. Wu, T.H. Ma, J. Cao, Y. Tian, A. Alabdulkarim, A comparative study of clustering ensemble algorithms, COMPUTERS & ELECTRICAL ENGINEERING 68 (2018) 603–615.

[30]

C. Wu, Z. Wang, A modified fuzzy dual-local information c-mean clustering algorithm using quadratic surface as prototype for image segmentation, Expert Systems with Application(Sep.) 201 (2022).

[31]

F. Yang, X. Li, Q. Li, T. Li, Exploring the diversity in cluster ensemble generation: Random sampling and random projection, Expert Systems with Applications 41 (10) (2014) 4844–4866,.

[32]

M. Ye, W. Liu, J. Wei, X. Hu, Fuzzy c-Means and Cluster Ensemble with Random Projection for Big Data Clustering, MATHEMATICAL PROBLEMS IN ENGINEERING 8 (9) (2016) 1–13,.

[33]

Z. Yu, L. Li, Y. Gao, J. You, J. Liu, H. Wong, …., G. Han, Hybrid clustering solution selection strategy, Pattern Recognition 47 (10) (2014) 3362–3375,.

[34]

Z.W. Yu, P.N. Luo, J.N. You, H.S. Wong, H. Leung, S. Wu, …., G.Q. Han, Incremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 28 (3) (2016) 701–714,.

Digital Library

[35]

Z, L., W, X., & C, S. (2012). Segmentation using superpixels: A bipartite graph partitioning approach. in 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[36]

F. Zhang, S. Qi, Q. Liu, M. Mao, A. Zeng, Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks, Expert Systems with Applications 149 (2020) 113346-,.

[37]

P. Zhou, L. Du, X. Liu, Y. Shen, M. Fan, X. Li, Self-Paced Clustering Ensemble, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32 (4) (2021) 1497–1511.

[38]

Zhou, P., Du, L., & Li, X. (2020). Self-paced Consensus Clustering with Bipartite Graph. in Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. https://doi.org/10.24963/ijcai.2020/291.

[39]

Zhou, P., Du, L., Shen, Y., & Li, X. (2021b). Tri-level Robust Clustering Ensemble with Multiple Graph Learning. in The 35th AAAI Conference on Artificial Intelligence (AAAI 2021).

[40]

J. Zhou, H. Zheng, L. Pan, Ensemble clustering based on dense representation, Neurocomputing 357 (2019) 66–76,.

Digital Library

Cited By

Wan JZhang KRan WMiao D(2024)A feasible solution model for manifold clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108635134:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108635
Wu ZCai MXu FLi Q(2024)PCS-granularity weighted ensemble clustering via Co-association matrixApplied Intelligence10.1007/s10489-024-05368-354:5(3884-3901)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10489-024-05368-3

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An improved weighted ensemble clustering based on two-tier uncertainty measurement

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 238, Issue PA

Mar 2024

1584 pages

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 27 February 2024

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Cited By

Wan JZhang KRan WMiao D(2024)A feasible solution model for manifold clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108635134:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108635
Wu ZCai MXu FLi Q(2024)PCS-granularity weighted ensemble clustering via Co-association matrixApplied Intelligence10.1007/s10489-024-05368-354:5(3884-3901)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10489-024-05368-3

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