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Incomplete multi-view clustering with multiple imputation and ensemble clustering

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Abstract

Multi-view clustering is an important and challenging task in machine learning and data mining. In the past decade, this topic attracted much attention and there have been many progress achieved in this field. However, in reality, due to different factors such as machine error, sensor failure, multi-view data are mostly incomplete, thus how to deal with this problem becomes a challenge. Some existing works mainly deal with view missing case, which means in certain view of datasets, the whole features of some samples would be lost. In fact, missing value can occur in any position, that is, any value missing case. In that case, there would be some values missed in any view with sheerly random way. We proposed a two-stage algorithm involved multiple imputation and ensemble clustering to deal with multi-view clustering in any value missing case. Multiple imputation is adopted to deal with missing values problem and weighted ensemble clustering is applied to implement multi-view clustering. The experimental comparison on several data sets verified the effectiveness of the proposed method.

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Notes

  1. https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set

  2. www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html

  3. http://archive.ics.uci.edu/ml/datasets/Multiple+Features

References

  1. Alqurashi T, Wang W (2018) Clustering ensemble method. Int J Machine Learn Cybern 10 (5):1–18

    Google Scholar 

  2. Ben-Hur W, Elisseeff A, Guyon I (2002) A stability based method for discovering structure in clustered data. Biocomputing 6–17

  3. Berikov V (2014) Weighted ensemble of algorithms for complex data clustering. Pattern Recogn Lett 39(3):99–106

    Article  Google Scholar 

  4. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on Computational learning theory, pp 92– 100

  5. Buuren SV, Groothuis-oudshoorn K (2011) Mice: Multivariate imputation by chained equations in r. J Stat Softw 45(3):1–67

    Article  Google Scholar 

  6. Cai D, He X, Han J, S.Huang T. (2011) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Machine Intell 33(8):1548–1560

    Article  Google Scholar 

  7. Chang X, Dacheng Tao CX (2007) A survey on multi-view learning arxiv

  8. Chao G, Sun J, Lu J, Wang AL, Langleben DD, Li CS, Bi J (2019) Multi-view cluster analysis with incomplete data to understand treatment effects. Inf Sci 494

  9. Chao G, Sun S (2016) Alternative multiview maximum entropy discrimination. IEEE Trans Neural Netw Learn Syst 27(7):1445–1456

    Article  MathSciNet  Google Scholar 

  10. Chao G, Sun S (2016) Consensus and complementarity based maximum entropy discrimination for multi-view classification. Inf Sci 367:296–310

    Article  Google Scholar 

  11. Chao G, Sun S (2018) Semi-supervised multi-view maximum entropy discrimination with expectation laplacian regularization. Inform Fusion 45:296–306

    Article  Google Scholar 

  12. Chao G (2018) Discriminative k-means laplacian clustering. Neural Process Lett 1–13

  13. Jie W, Yong X, Hong L (2020) Incomplete multiview spectral clustering with adaptive graph learning. IEEE Trans Cybern 50:1418–1429

    Article  Google Scholar 

  14. Sun S, Xie X, Dong C (2019) Multiview learning with generalized eigenvalue proximal support vector machines. IEEE Trans Cybern 49:688–697

    Article  Google Scholar 

  15. Xiang F, Yuchong H, Pan Z (2020) V3h:view variation and view heredity for incomplete multi-view clustering. IEEE Trans Artif Intell 1:233–247

    Article  Google Scholar 

  16. Xie X (2020) Multi-view semi-supervised least squares twin support vector machines with manifold-preserving graph reduction. Int J Machine Learn Cybern 11:2489–2499

    Article  Google Scholar 

  17. Xie X, Sun S (2019) General multi-view learning with maximum entropy discrimination. Neurocomputing 332:184–192

    Article  Google Scholar 

  18. Xie X, Sun S (2020) Multi-view support vector machines with the consensus and complementarity information. IEEE Trans Knowl Data Eng 32:2401–2413

    Article  Google Scholar 

  19. Xie Xijiong, Sun Shiliang (2020) General multi-view semi-supervised least squares support vector machines with multi-manifold regularization. Inf. Fusion 62:63–72

    Article  Google Scholar 

  20. Domeniconi C, Al-Razgan M (2009) Weighted cluster ensembles: Methods and analysis. ACM Trans Knowl Discov Data 2(4):1–40

    Article  Google Scholar 

  21. Fred AL, K.Jain A (2002) Data clustering using evidence accumulation. In: Proceedings of the 16th International Conference of Pattern Recognition, pp 276–280

  22. Fred AL, K.Jain A (2002) Evidence accumulation clustering based on the k-means algorithm. In: Proceedings of the Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition, pp 442–451

  23. Fred ALN, Jain AK (2005) Combining multiple clusterings using evidence accumulation. IEEE Trans Pattern Anal Machine Intell 27(6):835–50

    Article  Google Scholar 

  24. Guoqing C, Shiliang Sun JB (2021) A survey on multi-view clustering. IEEE Trans Artif Intell 2(2):146–168

    Article  Google Scholar 

  25. Handong Z, Zhengming Ding YF (2017) Multi-view clustering via deep matrix factorization. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp 2921– 2927

  26. Hu M, Chen S (2019) Doubly aligned incomplete multi-view clustering. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp 2262–2268

  27. Hussain SF, Mushtaq M, Halim Z (2014) Multi-view document clustering via ensemble method. J Intell Inform Syst 43(1):81–99

    Article  Google Scholar 

  28. Mirkin B (1997) Mathematical classification and clustering. J Oper Res Soc 48

  29. M.Rand W (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(4):846–850

    Article  Google Scholar 

  30. Nene SA, Nayar SK, Murase H, et al. (1996) Columbia object image library (COIL-20) Technical report CUCS-005-96

  31. Shao-Yuan Li YJ, Zhou ZH (2014) Partial multi-view clustering. In: Proceedings of the National Conference on Artificial Intelligence, vol 3, pp 1968–1974

  32. Strehl A, Ghosh J (2003) Cluster ensembles -a knowledge reuse framework for combining multiple partitions. J Machine Learn Res 3(3):583–617

    MathSciNet  MATH  Google Scholar 

  33. Sun S (2013) A survey of multi-view machine learning. Neural Comput Applic 23(7-8):2031–2038

    Article  Google Scholar 

  34. Tao Z, Liu H, Li S, Ding Z, Fu Y (2017) From ensemble clustering to multi-view clustering. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp 2843–2849

  35. Vega-Pons S, Ruiz-Shulcloper J (2011) A survey of clustering ensemble algorithm. Int J Pattern Recognit Artif Intell 25(3):337–372

    Article  MathSciNet  Google Scholar 

  36. Wang H, Zong L, Liu B, Yang Y, Zhou W (2019) Spectral perturbation meets incomplete multi-view data. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp 3677–3683

  37. Weixiang Shao LH, Yu PS (2015) Multiple incomplete views clustering via weighted nonnegative matrix factorization with l2,1 regularization. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases

  38. Xu W, Liu X, Gong Y (2003) Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 267–273

  39. Yang Y, Wang H (2018) Multi-view clustering: a survey. Big Data Mining and Analytics 1 (2):83–107

    Article  Google Scholar 

  40. Zhao J, Xie X, Xu X, Sun S (2017) Multi-view learning overview: Recent progress and new challenges. Inform Fusion 38:43–54

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported by the National Natural Science Foundation of China under Project 61772159, 61902090 and National Key Research and Development Project of China under Project 61832004.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, 2 West Culture, Weihai, Shandong Province, China

    Guoqing Chao, Shiming Yang, Chunshan Li & Dianhui Chu

  2. School of Information Science and Engineering, Harbin Institute of Technology, 2 West Culture, Weihai, Shandong Province, China

    Songtao Wang

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  1. Guoqing Chao
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  2. Songtao Wang
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  3. Shiming Yang
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  4. Chunshan Li
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  5. Dianhui Chu
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Correspondence to Dianhui Chu.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning

Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

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Chao, G., Wang, S., Yang, S. et al. Incomplete multi-view clustering with multiple imputation and ensemble clustering. Appl Intell 52, 14811–14821 (2022). https://doi.org/10.1007/s10489-021-02978-z

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