Abstract
Consensus clustering has in recent years become one of the most popular topics in the clustering research, due to its promising ability in combining multiple weak base clusterings into a strong consensus result. In this paper, we aim to deal with three challenging issues in consensus clustering, i.e., the high-order integration issue, the local reliability issue, and the efficiency issue. Specifically, we present a new consensus clustering approach termed meta-cluster based consensus clustering with local weighting and random walking (MC\(^3\)LR). To ensure the computational efficiency, we use the base clusters as the graph nodes to construct a cluster-wise similarity graph. Then, we perform random walks on the cluster-wise similarity graph to explore its high-order structural information, based on which a new cluster-wise similarity measure is derived. To tackle the local reliability issue, all of the base clusters are assessed and weighted according to the ensemble-driven cluster index (ECI). Finally, a locally weighted meta-clustering process is performed on the newly obtained cluster-wise similarity measure to build the consensus clustering result. Experimental results on multiple datasets have shown the effectiveness and efficiency of the proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bache, K., Lichman, M.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml
Fern, X.Z., Brodley, C.E.: Solving cluster ensemble problems by bipartite graph partitioning. In: Proceedings of International Conference on Machine Learning (ICML) (2004)
Fred, A.L.N., Jain, A.K.: Combining multiple clusterings using evidence accumulation. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 835–850 (2005)
Huang, D., Wang, C., Peng, H., Lai, J., Kwoh, C.: Enhanced ensemble clustering via fast propagation of cluster-wise similarities. IEEE Trans. Syst. Man Cybern.: Syst. (2018, in press). https://doi.org/10.1109/TSMC.2018.2876202
Huang, D., Wang, C., Wu, J., Lai, J., Kwoh, C.K.: Ultra-scalable spectral clustering and ensemble clustering. IEEE Trans. Knowl. Data Eng. (2019, in press). https://doi.org/10.1109/TKDE.2019.2903410
Huang, D., Wang, C.D., Lai, J.H.: Locally weighted ensemble clustering. IEEE Trans. Cybern. 48(5), 1460–1473 (2018)
Huang, D., Lai, J.H., Wang, C.D.: Exploiting the wisdom of crowd: a multi-granularity approach to clustering ensemble. In: Proceedings of International Conference on Intelligence Science and Big Data Engineering (IScIDE), pp. 112–119 (2013)
Huang, D., Lai, J.H., Wang, C.D.: Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis. Neurocomputing 170, 240–250 (2015)
Huang, D., Lai, J.H., Wang, C.D.: Robust ensemble clustering using probability trajectories. IEEE Trans. Knowl. Data Eng. 28(5), 1312–1326 (2016)
Huang, D., Lai, J.H., Wang, C.D., Yuen, P.C.: Ensembling over-segmentations: from weak evidence to strong segmentation. Neurocomputing 207, 416–427 (2016)
Huang, D., Lai, J., Wang, C.D.: Ensemble clustering using factor graph. Pattern Recogn. 50, 131–142 (2016)
Huang, D., Wang, C.D., Lai, J.H.: LWMC: a locally weighted meta-clustering algorithm for ensemble clustering. In: Proceedings of International Conference on Neural Information Processing (ICONIP), pp. 167–176 (2017)
Iam-On, N., Boongoen, T., Garrett, S., Price, C.: A link-based approach to the cluster ensemble problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(12), 2396–2409 (2011)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Levandowsky, M., Winter, D.: Distance between sets. Nature 234, 34–35 (1971)
Liu, H., Liu, T., Wu, J., Tao, D., Fu, Y.: Spectral ensemble clustering. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 715–724 (2015)
Liu, H., Zhao, R., Fang, H., Cheng, F., Fu, Y., Liu, Y.Y.: Entropy-based consensus clustering for patient stratification. Bioinformatics 33(17), 2691–2698 (2017)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Strehl, A., Ghosh, J.: Cluster ensembles: a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)
Topchy, A., Jain, A.K., Punch, W.: Clustering ensembles: models of consensus and weak partitions. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1866–1881 (2005)
Wu, J., Liu, H., Xiong, H., Cao, J., Chen, J.: K-means-based consensus clustering: a unified view. IEEE Trans. Knowl. Data Eng. 27(1), 155–169 (2015)
Yi, J., Yang, T., Jin, R., Jain, A.K.: Robust ensemble clustering by matrix completion. In: Proceedings of IEEE International Conference on Data Mining (ICDM) (2012)
Acknowledgments
This work was supported by NSFC (61976097 & 61602189).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
He, N., Huang, D. (2019). Meta-cluster Based Consensus Clustering with Local Weighting and Random Walking. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-36204-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36203-4
Online ISBN: 978-3-030-36204-1
eBook Packages: Computer ScienceComputer Science (R0)