article

Clusterer ensemble

Authors:

Zhi-Hua Zhou,

Wei TangAuthors Info & Claims

Knowledge-Based Systems, Volume 19, Issue 1

Pages 77 - 83

https://doi.org/10.1016/j.knosys.2005.11.003

Published: 01 March 2006 Publication History

Abstract

Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods for unsupervised learning. Here, an ensemble comprises multiple clusterers, each of which is trained by k-means algorithm with different initial points. The clusters discovered by different clusterers are aligned, i.e. similar clusters are assigned with the same label, by counting their overlapped data items. Then, four methods are developed to combine the aligned clusterers. Experiments show that clustering performance could be significantly improved by ensemble methods, where utilizing mutual information to select a subset of clusterers for weighted voting is a nice choice. Since the proposed methods work by analyzing the clustering results instead of the internal mechanisms of the component clusterers, they are applicable to diverse kinds of clustering algorithms.

References

[1]

Estivill-Castro, V., Why so many clustering algorithms-a position paper. SIGKDD Explorations. v4 i1. 65-75.

Crossref

Google Scholar

[2]

Dietterich, T.G., Ensemble learning. In: Arbib, M.A. (Ed.), The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, MA.

Google Scholar

[3]

Hansen, L.K. and Salamon, P., Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence. v12 i10. 993-1001.

Crossref

Google Scholar

[4]

F.J. Huang, Z.-H. Zhou, H.-J. Zhang, T. Chen, Pose invariant face recognition, Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, 2000, pp. 245-250.

Crossref

Google Scholar

[5]

Drucker, H., Schapire, R. and Simard, P., Improving performance in neural networks using a boosting algorithm. In: Hanson, S.J., Cowan, J.D., Lee Giles, C. (Eds.), Advances in Neural Information Processing Systems, 5. Morgan Kaufmann, San Mateo, CA. pp. 42-49.

Crossref

Google Scholar

[6]

K.J. Cherkauer, Human expert level performance on a scientific image analysis task by a system using combined artificial neural networks, Proceedings of the 13th AAAI Workshop on Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms, Portland, OR, 1996, pp. 15-21.

Google Scholar

[7]

Zhou, Z.-H., Jiang, Y., Yang, Y.-B. and Chen, S.-F., Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine. v24 i1. 25-36.

Crossref

Google Scholar

[8]

Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Proceedings of the second European Conference on Computational Learning Theory, Barcelona, Spain, 1995, pp. 23-37.

Digital Library

Google Scholar

[9]

J. MacQueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, vol. 1, 1967, pp. 281-297.

Google Scholar

[10]

A. Strehl, J. Ghosh, R.J. Mooney, Impact of similarity measures on web-page clustering, Proceedings of the AAAI2000 Workshop on AI for Web Search, Austin, TX, 2000, pp. 58-64.

Google Scholar

[11]

Zhou, Z.-H., Wu, J. and Tang, W., Ensembling neural networks: many could be better than all. Artificial Intelligence. v137 i1-2. 239-263.

Crossref

Google Scholar

[12]

C. Blake, E. Keogh, C.J. Merz, UCI repository of machine learning databases {http://www.ics.uci.edu/~mlearn/MLRepository.htm}, Department of Information and Computer Science, University of California, Irvine, CA, 1998.

Google Scholar

[13]

D.S. Modha, W.S. Spangler, Feature weighting in k-means clustering, Machine Learning, 52 (3) (2003) 217-237.

Crossref

Google Scholar

[14]

Breiman, L., Bagging predictors. Machine Learning. v24 i2. 123-140.

Crossref

Google Scholar

Cited By

View all

Zhou PHu BYan DDu L(2024)Clustering Ensemble via Diffusion on Adaptive MultiplexIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331140936:4(1463-1474)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3311409
Zhou PDu LLiu XLing ZJi XLi XShen Y(2023)Partial Clustering EnsembleIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332191336:5(2096-2109)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3321913
Shi YYang KYu ZChen CZeng H(2023)Adaptive Ensemble Clustering With Boosting BLS-Based AutoencoderIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327112035:12(12369-12383)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3271120
Show More Cited By

Clusterer ensemble
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

Recommendations

Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption
Abstract
This paper presents a comparison of the impact of various unsupervised ensemble learning methods on electricity load forecasting. The electricity load from consumers is simply aggregated or optimally clustered to more predictable groups by cluster ...
Markov clustering ensemble
Abstract
Clustering ensemble is an unsupervised ensemble learning method that is very important in machine learning, since it integrates multiple weak base clustering results to produce a strong consistency result. This paper proposes the ...
Spectral co-clustering ensemble

The goal of co-clustering is to simultaneously cluster the rows and columns of an input data matrix. It overcomes several limitations associated with traditional clustering methods by allowing automatic discovery of similarity based on a subset of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Knowledge-Based Systems Volume 19, Issue 1

March, 2006

104 pages

ISSN:0950-7051

Issue’s Table of Contents

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 March 2006

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

38
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 01 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhou PHu BYan DDu L(2024)Clustering Ensemble via Diffusion on Adaptive MultiplexIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331140936:4(1463-1474)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3311409
Zhou PDu LLiu XLing ZJi XLi XShen Y(2023)Partial Clustering EnsembleIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332191336:5(2096-2109)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3321913
Shi YYang KYu ZChen CZeng H(2023)Adaptive Ensemble Clustering With Boosting BLS-Based AutoencoderIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327112035:12(12369-12383)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3271120
Zhou PDu LLi X(2023)Adaptive Consensus Clustering for Multiple K-Means Via Base Results RefiningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326497035:10(10251-10264)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3264970
Wu DLi JMo XWu W(2023)DFECTS: A Deep Fuzzy Ensemble Clusterer for Time SeriesAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0834-5_5(61-80)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-97-0834-5_5
Sun YWang SHuang DSun YHu ASun J(2022)A multiple hierarchical clustering ensemble algorithm to recognize clusters arbitrarily shapedIntelligent Data Analysis10.3233/IDA-21611226:5(1211-1228)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDA-216112
Du HWang WBai LFeng J(2022)A Generative Clustering Ensemble Model and Its Application in IoT Data AnalysisWireless Communications & Mobile Computing10.1155/2022/80811772022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8081177
Zhou PLiu XDu LLi X(2022)Self-paced Adaptive Bipartite Graph Learning for Consensus ClusteringACM Transactions on Knowledge Discovery from Data10.1145/356470117:5(1-35)Online publication date: 27-Sep-2022
https://dl.acm.org/doi/10.1145/3564701
Xu LDing S(2021)A novel clustering ensemble model based on granular computingApplied Intelligence10.1007/s10489-020-01979-851:8(5474-5488)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s10489-020-01979-8
Li GMahmoudi MQasem STuan BPho K(2020)Cluster ensemble of valid small clustersJournal of Intelligent & Fuzzy Systems10.3233/JIFS-19153039:1(525-542)Online publication date: 17-Jul-2020
https://doi.org/10.3233/JIFS-191530
Show More Cited By

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Recommendations

Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption

Markov clustering ensemble

Spectral co-clustering ensemble

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations