Abstract
A new semi-supervised clustering framework for uncertain multi-view data is proposed inspired by the theory of three-way decisions, which is an alternative formulation different from the ones used in the existing studies. A cluster is represented by three regions such as the core region, fringe region and trivial region. The three-way representation intuitively shows which objects are fringe to the cluster. The proposed method is an iterative processing which includes two parts: (1) the three-way spectral clustering algorithm which is devised to obtain the three-way representation result; and (2) the active learning strategy which is designed to obtain the prior supervision information from the fringe regions, and the pairwise constraints information is used to adjust the similarity matrix between objects. Experimental results show that the proposed method can cluster multi-view data effectively and is better in performances than the compared single-view clusterings and other semi-supervised clustering approaches.
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References
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceeding of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100. ACM (1998)
Bickel, S., Scheffer, T.: Multi-view clustering. In: ICDM, vol. 4, pp. 19–26 (2004)
Chaudhuri, K., Kakade, S.M., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 129–136. ACM (2009)
Chen, W., Feng, G.: Spectral clustering: a semi-supervised approach. Neurocomputing 77(1), 229–242 (2012)
Ding, S., Jia, H., Zhang, L., Jin, F.: Research of semi-supervised spectral clustering algorithm based on pairwise constraints. Neural Comput. Appl. 24(1), 211–219 (2014)
Grira, N., Crucianu, M., Boujemaa, N.: Unsupervised and semi-supervised clustering: a brief survey (2005)
Klein, D., Kamvar, S.D., Manning, C.D.: From instrance-level constraints to space-level constraints: making the most of prior knowledge in data clustering. Stanford (2002)
Li, Y., Nie, F., Huang, H., Huang, J.: Large-scale multi-view spectral clustering via bipartite graph. In: AAAI, pp. 2750–2756 (2015)
Liang, D., Liu, D.: Systematic studies on three-way decisions with interval-valued decision-theoretic rough sets. Inf. Sci. 276, 186–203 (2014)
Lingras, P., Yan, R.: Interval clustering using fuzzy and rough set theory. In: Proceedings 2004 IEEE Annual Meeting, Fuzzy Information, Banff, Alberta, pp. 780–784 (2004)
Lingras, P., West, C.: Interval set clustering of web users with rough k-means. J. Intell. Inf. Syst. 23(1), 5–16 (2004)
Liu, J., Wang, C., Guo, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceeding of the 2013 SIAM International Conference on Data Mining, pp. 252–260. Society for Industrial and Applied Mathematics (2013)
Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)
Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: AAAI/IAAI, vol. 1097 (2000)
Wang, W., Zhou, Z.H.: On multi-view active learning and the combination with semi-supervised learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1152–1159. ACM (2008)
Wang, J., Wang, X., Tian, F., Liu, C.H., Yu, H., Liu, Y.: Adaptive multi-view semi-supervised nonnegative matrix factorization. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9948, pp. 435–444. Springer, Cham (2016). doi:10.1007/978-3-319-46672-9_49
Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 40(6), 1438–1446 (2010)
Xiong, S., Azimi, J., Fern, X.Z.: Active learning of constraints for semi-supervised clustering. IEEE Trans. Knowl. Data Eng. 26(1), 43–54 (2014)
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. arXiv preprint arXiv:1304.5634 (2013)
Yao, Y., Lingras, P., Wang, R., Miao, D.: Interval set cluster analysis: a re-formulation. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS, vol. 5908, pp. 398–405. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10646-0_48
Yao, Y.: An outline of a theory of three-way decisions. In: Yao, J.T., Yang, Y., Słowiński, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) RSCTC 2012. LNCS, vol. 7413, pp. 1–17. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32115-3_1
Yao, Y.: Three-way decisions and cognitive computing. Cogn. Comput. 8(4), 543–554 (2016)
Yao, J., Azam, N.: Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Trans. Fuzzy Syst. 23(1), 3–15 (2015)
Ye, X., Sakurai, T.: Robust similairty measure for spectral clustering based on shared neighbors. ETRI J. 38(3), 540–550 (2016)
Yu, H., Wang, G.Y., Li, T.R., Liang, J.Y., Miao, D.Q., Yao, Y.Y.: Three-Way Decisions: Methods and Practices for Complex Problem Solving. Science Press, Beijing (2015). (in Chinese)
Yu, H., Zhang, C., Wang, G.: A tree-based incremental overlapping clustering method using the three-way decision theory. Knowl.-Based Syst. 91, 189–203 (2016)
Yu, H., et al.: Methods and practices of three-way decisions for complex problem solving. In: Ciucci, D., Wang, G., Mitra, S., Wu, W.-Z. (eds.) RSKT 2015. LNCS, vol. 9436, pp. 255–265. Springer, Cham (2015). doi:10.1007/978-3-319-25754-9_23
Zhang, X., Zhao, D.Y., Wei, S., Xiao, W.X.: Active semi-supervised clustering based on multi-view learning. In: WRI Global Congress on Intelligent Systems, GCIS 2009, vol. 3, pp. 495–499. IEEE (2009)
Zhou, B., Yao, Y., Luo, J.: Cost-sensitive three-way email spam fitering. J. Intell. Inf. Syst. 42(1), 19–45 (2014)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61379114 & 61533020.
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Yu, H., Wang, X., Wang, G. (2017). A Semi-supervised Three-Way Clustering Framework for Multi-view Data. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_23
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