Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values

Information Sciences

Volumn 299, Issue , 2015, Pages 221-242

  Luo, Chuan ^a,b   Li, Tianrui ^a   Chen, Hongmei ^a   Lu, Lixia ^c  

^a SOUTHWEST JIAOTONG UNIVERSITY   (China)

^b UNIVERSITY OF REGINA   (Canada)

^c Shandong Polytechnic   (China)

Author keywords

Incremental learning; Knowledge discovery; Rough set; Set valued decision systems

Indexed keywords

ALGORITHMS; APPROXIMATION ALGORITHMS; COMPUTATION THEORY; DATA MINING; SET THEORY; VIRTUAL REALITY;

ARTIFICIAL DATASETS; DECISION SYSTEMS; DYNAMIC MAINTENANCES; DYNAMIC VARIATIONS; INCREMENTAL ALGORITHM; INCREMENTAL LEARNING; ROUGH APPROXIMATIONS; UPPER AND LOWER APPROXIMATION;

ROUGH SET THEORY;

EID: 84961287981     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2014.12.029     Document Type: Article

References (55)

1. F. Altiparmak, E. Tuncel, and H. Ferhatosmanoglu Incremental maintenance of online summaries over multiple streams IEEE Trans. Knowl. Data Eng. 20 2 2008 216 229
2. V.S. Ananthanarayana, M.M. Narasimha, and D.K. Subramanian Tree structure for efficient data mining using rough sets Pattern Recogn. Lett. 24 2003 851 862
3. C. Chan A rough set approach to attribute generalization in data mining Inform. Sci. 107 1-4 1998 169 176
4. H.M. Chen, T.R. Li, C. Luo, S.J. Horng, and G.Y. Wang A rough set-based method for updating decision rules on attribute values' coarsening and refining IEEE Trans. Knowl. Data Eng. 2014 10.1109/TKDE.2014.2320740
5. H.M. Chen, T.R. Li, and D. Ruan Dynamic maintenance of approximations under a rough-set based variable precision limited tolerance relation J. Multi-valued Logic Soft Comput. 18 2012 577 598
6. H.M. Chen, T.R. Li, and D. Ruan Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining Knowl.-Based Syst. 31 2012 140 161
7. H.M. Chen, T.R. Li, D. Ruan, J.H. Lin, and C.X. Hu A rough-set based incremental approach for updating approximations under dynamic maintenance environments IEEE Trans. Knowl. Data Eng. 25 2 2013 274 284
8. P. Dey, S. Dey, S. Datta, and J. Sil Dynamic discreduction using rough sets Appl. Soft Comput. 11 5 2011 3887 3897
9. Y.N. Fan, T.L. Tseng, C.C. Chern, and C.C. Huang Rule induction based on an incremental rough set Expert Syst. Appl. 36 9 2009 11439 11450
10. S. Greco, B. Matarazzo, and R. Slowinski Rough set theory for multicriteria decision analysis Eur. J. Oper. Res. 129 2001 1 47
11. C.C. Huang, T.L. Tseng, Y.N. Fan, and C.H. Hsu Alternative rule induction methods based on incremental objet using rough set theory Appl. Soft Comput. 13 1 2013 372 389
12. Y. He, F. Naughton, Anonymization of set-valued data via top-down, local generalization, in: Proceedings of the 35th International Conference on Very Large Data Bases, 2009, pp. 934-945.
13. G. Kou, K. Miettinen, and Y. Shi Multiple criteria decision making: challenges and advancements J. Multi-Criteria Decis. Anal. 18 2011 1 4
14. C. Luo, T.R. Li, and H.M. Chen Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization Inform. Sci. 257 2014 210 228
15. C. Luo, T.R. Li, H.M. Chen, and D. Liu Incremental approaches for updating approximations in set-valued ordered information systems Knowl.-Based Syst. 50 2013 218 233
16. S.Y. Li, T.R. Li, and D. Liu Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set Knowl.-Based Syst. 40 2013 17 26
17. S.Y. Li, T.R. Li, and D. Liu Dynamic maintenance of approximations in dominance-based rough set approach under the variation of the object set Int. J. Intell. Syst. 28 8 2013 729 751
18. T.R. Li, D. Ruan, W. Geert, J. Song, and Y. Xu A rough sets based characteristic relation approach for dynamic attribute generalization in data ming Knowl.-Based Syst. 20 2007 485 494
19. W.Y. Lin, S.A. Alvarez, and C. Ruiz Efficient adaptive-support association rule mining for recommender systems Data Min. Knowl. Discovery 6 1 2002 83 105
20. L. Polkowski, S. Tsumoto, and T.Y. Lin Rough Set Methods and Applications 2000 Physica-Verlag Berlin
21. W. Pedrycz Granular Computing: Analysis and Design of Intelligent Systems 2013 CRC Press/Francis Taylor Boca Raton
22. W. Pedrycz Allocation of information granularity in optimization and decision making models: towards building the foundations of granular computing Eur. J. Oper. Res. 232 1 2014 137 145
23. W. Pedrycz, and W. Homenda Building the fundamentals of granular computing: a principle of justifiable granularity Appl. Soft Comput. 13 10 2013 4209 4218
24. Z. Pawlak Rough sets Int. J. Comput. Inform. Sci. 11 1982 341 356
25. Z. Pawlak Rough set theory and its application to data analysis Cybernet. Syst.: Int. J. 29 1998 661 668
26. Y.H. Qian, C.Y. Dang, J.Y. Liang, and D.W. Tang Set-valued ordered information systems Inform. Sci. 179 2009 2809 2832
27. Y.H. Qian, J.Y. Liang, P. Song, and C.Y. Dang On dominance relations in disjunctive set-valued ordered information systems Int. J. Inform. Technol. Decis. Making 9 2010 9 33
28. Y.H. Qian, J.Y. Liang, and C.Y. Dang Interval ordered information systems Comput. Math. Appl. 56 2008 1994 2009
29. V. Raghavan, and A. Hafez Dynamic data mining R. Loganantharaj, G. Palm, M. Ali, Intelligent Problem Solving-Methodologies and Approaches: Proc. of Thirteenth International Conference on Industrial Engineering Applications of AI & Expert Systems 2000 Springer New York 220 229
30. C.A. Shoemaker, C. Ruiz, Association rule mining algorithms for set-valued data, in: Proceedings of the 4th International Conference on Intelligent Data Engineering and Automated Learning, 2003, pp. 669-676.
31. L. Sun, J.C. Xu, and Y. Tian Feature selection using rough entropy-based uncertainty measures in incomplete decision systems Knowl.-Based Syst. 36 2012 206 216
32. R.W. Swiniarski, and A. Skowron Rough set methods in feature selection and recognition Pattern Recogn. Lett. 24 6 2003 833 849
33. M.W. Shao, and W.X. Zhang Dominance relation and rules in an incomplete ordered information system Int. J. Intell. Syst. 20 2005 13 27
34. S. Tsumoto Automated extraction of medical expertsystem rules from clinical databases based on roughset theory Inform. Sci. 112 1998 67 84
35. F. Wang, J.Y. Liang, and Y.H. Qian Attribute reduction: a dimension incremental strategy Knowl.-Based Syst. 39 2013 95 108
36. W.H. Xu, Y.F. Liu, and T.J. Li Intuitionistic fuzzy ordered information system Int. J. Uncert. Fuzziness Knowl.-Based Syst. 21 3 2013 10.1142/S0218488513500190
37. W.H. Xu, Y. Li, and X.W. Liao Approaches to attribute based on rough set and matrix computation in inconsistent ordered information systems Knowl.-Based Syst. 27 2012 78 91
38. Y.T. Xu, L.S. Wang, and R.Y. Zhang A dynamic attribute reduction algorithm based on 0-1 integer programming Knowl.-Based Syst. 24 8 2011 1341 1347
39. M. Yang An incremental updating algorithm of the computation of a core based on the improved discernibility matrix Chinese J. Comput. 29 3 2006 407 413
40. X.B. Yang, D.J. Yu, J.Y. Yang, and L.H. Wei Dominance-based rough set approach to incomplete interval-valued information system Data Knowl. Eng. 68 11 2009 1331 1347
41. X.B. Yang, J.Y. Yang, C. Wu, and D.J. Yu Dominance-based rough set approach and knowledge reductions in incomplete ordered information system Inform. Sci. 178 4 2008 1219 1234
42. Y. Sai, Y.Y. Yao, N. Zhong, Data analysis and mining in ordered information table, in: Proceedings of the 2001 IEEE International Conference on Data Mining, 2001, pp. 497-504.
43. Y.Y. Yao and N. Zhong, Potential applications of granular computing in knowledge discovery and data mining, in: Proceedings of the World Multiconf. Systemics, Cybernetics and Informatics, 1999, pp. 573-580.
44. Y.Y. Yao Three-way decisions with probabilistic rough sets Inform. Sci. 180 2010 341 353
45. Y.Y. Yao Information granulation and rough set approximation Int. J. Intell. Syst. 16 1 2001 87 104
46. Y.Y. Yao, An outline of a theory of three-way decisions, in: Proceedings of the 8th International Conference on Rough Sets and Current Trends in Computing, vol. 7413, 2012, pp. 1-17.
47. Y.Y. Yao The superiority of three-way decisions in probabilistic rough set models Inform. Sci. 181 6 2011 1080 1096
48. Y.Y. Yao, and X.F. Deng Quantitative rough sets based on subsethood measures Inform. Sci. 267 2014 306 322
49. Y.Y. Yao, and Y. Zhao Attribute reduction in decision-theoretic rough set models Inform. Sci. 178 17 2008 3356 3373
50. J.B. Zhang, T.R. Li, D. Ruan, and D. Liu Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems Int. J. Approx. Reason. 53 2012 620 635
51. J.B. Zhang, T.R. Li, D. Ruan, and D. Liu Neighborhood rough sets for dynamic data mining Int. J. Intell. Syst. 27 2012 317 342
52. J.B. Zhang, J.S. Wong, Y. Pan, and T.R. Li A parallel matrix-based method for computing approximations in incomplete information systems IEEE Trans. Knowl. Data Eng. 2014 10.1109/TKDE.2014.2330821
53. J.B. Zhang, J.S. Wong, T.R. Li, and Y. Pan A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems Int. J. Approx. Reason. 55 3 2014 896 907
54. J.B. Zhang, T.R. Li, and H.M. Chen Composite rough sets for dynamic data mining Inform. Sci. 257 2014 81 100
55. Datasets in the article, < http://www2.cs.uregina.ca/∼luo256/datasets.html >.