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Multitask possibilistic and fuzzy co-clustering algorithm for clustering data with multisource features

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Abstract

People often encounter two major problems for the practical clustering problems. One is the problem arising from improper extraction of feature sets, such as the weakness of the features and the feature vector usually has the property of high-dimensional and multisource. The other is that the outliers interfere with the clustering results. In this paper, we use the idea of co-clustering to cluster datasets and feature sources at the same time, and use the information which received from the information sharing between tasks to improve the accuracy of clustering tasks through the idea of multitask. And we used the advantage of the typical degree to construct a new parameter selection index to identify the outliers, and to correct each parameter by weakening the influence of the identified outliers on the clustering results. In order to reflect the applicability and robustness of the algorithm, we extend the algorithm to the non-precise dataset and evaluate the algorithm from multiple aspects through experiments. Experiments show that the proposed algorithms not only improve the clustering accuracy, but also greatly reduce the interference of outliers to clustering results.

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References

  1. Pedrycz W (2002) Collaborative fuzzy clustering. Pattern Recognit Lett 23(14):1675–1686

    Article  Google Scholar 

  2. Loia V, Pedrycz W, Senatore S (2007) Semantic web content analysis: a study in proximity-based collaborative clustering. IEEE Trans Fuzzy Syst 15(6):1294–1312

    Article  Google Scholar 

  3. Coletta Luiz FS, Vendramin L, Hruschka ER, Campello Ricardo JGB, Pedrycz W (2012) Collaborative fuzzy clustering algorithms: some refinements and design guidelines. IEEE Trans Fuzzy Syst 20(3):444–462

    Article  Google Scholar 

  4. Mandhani B, Joshi S, Kummamuru K (2003) A matrix density based algorithm to hierarchically co-cluster documents and words. In: International Conference on World Wide Web, pp 511–518. https://doi.org/10.1145/775152.775225

  5. Tjhi W-C, Chen L (2008) Dual fuzzy-possibilistic coclustering for categorization of documents. IEEE Trans Fuzzy Syst 17(3):532–543. https://doi.org/10.1109/TFUZZ.2008.924332

    Article  Google Scholar 

  6. Yan Y, Chen L, Tjhi W-C (2013) Fuzzy semi-supervised co-clustering for text documents. Fuzzy Sets Syst 215:74–89. https://doi.org/10.1016/j.fss.2012.10.016

    Article  MathSciNet  Google Scholar 

  7. Huang S, Wang H, Lib D, Yang Y, Li T (2015) Spectral co-clustering ensemble. Knowl Based Syst 84:46–55

    Article  Google Scholar 

  8. Laclau C, Nadif M (2016) Hard and fuzzy diagonal co-clustering for document-term partitioning. Neurocomputing 193:133–147

    Article  Google Scholar 

  9. Zhang J, Zhang C (2011) Multitask Bregman clustering. Neurocomputing 74(10):1720–1734. https://doi.org/10.1016/j.neucom.2011.02.004

    Article  MathSciNet  Google Scholar 

  10. Zhang Z, Zhou J (2012) Multi-task clustering via domain adaptation. Pattern Recognit 45(1):465–473. https://doi.org/10.1016/j.patcog.2011.05.011

    Article  MATH  Google Scholar 

  11. Zhang X, Zhang X (2013) Smart multi-task Bregman clustering and multi-task kernel clustering. In: Proceedings of the Twenty-Seventh AAAI conference on artificial intelligence, pp 1034–1040

  12. Huy TN, Shao H, Tong B, Suzuki E (2013) A feature-free and parameter-light multi-task clustering framework. Knowl Inf Syst 36(1):251–276. https://doi.org/10.1007/s10115-012-0550-5

    Article  Google Scholar 

  13. Yang P, Huang K, Liu C-L (2013) A multi-task framework for metric learning with common subspace. Neural Comput Appl 22(7–8):1337–1347. https://doi.org/10.1007/s00521-012-0956-8

    Article  Google Scholar 

  14. Tang X, Miao Q, Quan Y, Tang J, Deng K (2015) Predicting individual retweet behavior by user similarity: a multi-task learning approach. Knowl Based Syst. 89:681–688. https://doi.org/10.1016/j.knosys.2015.09.008

    Article  Google Scholar 

  15. Sokhandan A, Adibi P, Sajadi M (2017) Multitask fuzzy Bregman co-clustering approach for clustering data with multisource features. Neurocomputing 247:102–114. https://doi.org/10.1016/j.neucom.2017.03.062

    Article  Google Scholar 

  16. Yang M-S, Ko C-H (1996) On a class of fuzzy c-numbers clustering procedures for fuzzy data. Fuzzy Sets Syst 84(1):49–60. https://doi.org/10.1016/0165-0114(95)00308-8

    Article  MathSciNet  MATH  Google Scholar 

  17. Hathaway RJ, Bezdek JC, Yingkang H (2000) Generalized fuzzy c-means clustering strategies using Lp norm distances. IEEE Trans Fuzzy Syst 8(5):576–582. https://doi.org/10.1109/91.873580

    Article  Google Scholar 

  18. Lim CP, Kuan MM, Harrison RF (2005) Application of fuzzy ARTMAP and fuzzy c-means clustering to pattern classification with incomplete data. Neural Comput Appl 14(2):104–113. https://doi.org/10.1007/s00521-004-0445-9

    Article  Google Scholar 

  19. Zhang H, Jing L (2009) Semi-supervised fuzzy clustering: a kernel-based approach. Knowl Based Syst 22(6):477–481. https://doi.org/10.1016/j.knosys.2009.06.009

    Article  Google Scholar 

  20. Lam Y-K, Tsang PWM, Leung C-S (2013) PSO-based K-means clustering with enhanced cluster matching for gene expression data. Neural Comput Appl 22(7–8):1349–1355. https://doi.org/10.1007/s00521-012-0959-5

    Article  Google Scholar 

  21. Izakian H, Pedrycz W (2014) Agreement-based fuzzy C-means for clustering data with blocks of features. Neurocomputing 127:266–280. https://doi.org/10.1016/j.neucom.2013.08.006

    Article  Google Scholar 

  22. Hung W-L, Yang J-H (2015) Automatic clustering algorithm for fuzzy data. J Appl Stat 42(7):1503–1518. https://doi.org/10.1080/02664763.2014.1001326

    Article  MathSciNet  Google Scholar 

  23. Zhang H, Wang S, Xu X, Chow TWS, Wu QJ (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2018.2797060.

  24. Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98–110. https://doi.org/10.1109/91.227387

    Article  Google Scholar 

  25. Timm H, Borgelt C, Doring C, Kruse R (2004) An extension to possibilistic fuzzy cluster analysis. Fuzzy Sets Syst 147(1):3–16. https://doi.org/10.1016/j.fss.2003.11.009

    Article  MathSciNet  MATH  Google Scholar 

  26. Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530. https://doi.org/10.1109/TFUZZ.2004.840099

    Article  Google Scholar 

  27. Yang M-S, Wub K-L (2006) Unsupervised possibilistic clustering. Pattern Recognit J Pattern Recognit Soc 39(1):5–21

    Article  Google Scholar 

  28. Xie Z, Wang S, Chung FL (2008) An enhanced possibilistic C-Means clustering algorithm EPCM. Soft Comput 12(6):593–611. https://doi.org/10.1007/s00500-007-0231-6

    Article  MATH  Google Scholar 

  29. Hamasuna Y, Endo Y, Miyamoto S (2010) On tolerant fuzzy c-means clustering and tolerant possibilistic clustering. Soft Comput 14(5):487–494. https://doi.org/10.1007/s00500-009-0451-z

    Article  Google Scholar 

  30. Ferraro MB, Giordani P (2017) Possibilistic and fuzzy clustering methods for robust analysis of non-precise data. Int J Approx Reason 88:23–38. https://doi.org/10.1016/j.ijar.2017.05.002

    Article  MathSciNet  MATH  Google Scholar 

  31. Shanthi I, Valarmathi ML (2013) SAR image despeckling using possibilistic fuzzy C-means clustering and edge detection in bandelet domain. Neural Comput Appl 23:279–291. https://doi.org/10.1007/s00521-013-1394-y

    Article  Google Scholar 

  32. Kannan SR, Devi R, Ramathilagam S, Hong TP (2017) Effective fuzzy possibilistic c-means: an analyzing cancer medical database. Soft Comput 21(11):2835–2845. https://doi.org/10.1007/s00500-016-2198-7

    Article  Google Scholar 

  33. Truong HQ, Ngo LT, Pedrycz W (2017) Granular fuzzy possibilistic C-means clustering approach to DNA microarray problem. Knowl Based Syst 133:53–65. https://doi.org/10.1016/j.knosys.2017.06.019

    Article  Google Scholar 

  34. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(8):841–847

    Article  Google Scholar 

  35. Pedrycz W, Bezdek JC, Hathaway RJ, Rogers GW (1998) Two nonparametric models for fusing heterogeneous fuzzy data. IEEE Trans Fuzzy Syst 6(3):411–425. https://doi.org/10.1109/91.705509

    Article  Google Scholar 

  36. Hung W-L, Yang M-S (2005) Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation. Fuzzy Sets Syst 150(3):561–577. https://doi.org/10.1016/j.fss.2004.04.007

    Article  MathSciNet  MATH  Google Scholar 

  37. Quost B, Denoeux T (2010) Clustering fuzzy data using the fuzzy EM algorithm. Scalable Uncertain Manag 6379:333–346

    Article  Google Scholar 

  38. Zarandi MHF, Razaee ZS (2011) A fuzzy clustering model for fuzzy data with outliers. Int J Fuzzy Syst Appl 1(2):29–42. https://doi.org/10.4018/ijfsa.2011040103

    Article  Google Scholar 

  39. Coppi R, DUrso P, Giordani P (2012) Fuzzy and possibilistic clustering for fuzzy data. Comput Stat Data Anal 56(4):915–927. https://doi.org/10.1016/j.csda.2010.09.013

    Article  MathSciNet  MATH  Google Scholar 

  40. Zaki MJ, Meira W Jr (2014) Data mining and analysis: foundamental concepts and algorithms. Cambridge University Press, Cambridge, pp 425–428

    Book  Google Scholar 

  41. Zhang H, Chow TWS, Wu QMJ (2016) Organizing books and authors by multilayer SOM. IEEE Transa Neural Netw Learn Syst 27(12):2537–2550. https://doi.org/10.1109/TNNLS.2015.2496281

    Article  Google Scholar 

  42. Che J, Yang Y, Li L, Bai X, Zhang S, Deng C (2017) Maximum relevance minimum common redundancy feature selection for nonlinear data. Inf Sci 409–410(10):68–86. https://doi.org/10.1016/j.ins.2017.05.013

    Article  MATH  Google Scholar 

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Acknowledgements

We are grateful to anonymous reviewers for their critical and valuable comments. This work was supported by the National Natural Science Foundation of China (Grant 61573266).

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  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710126, Shaanxi, China

    Jiaqi Ren & Youlong Yang

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  1. Jiaqi Ren
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Correspondence to Jiaqi Ren.

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Ren, J., Yang, Y. Multitask possibilistic and fuzzy co-clustering algorithm for clustering data with multisource features. Neural Comput & Applic 32, 4785–4804 (2020). https://doi.org/10.1007/s00521-018-3851-0

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