Recently, X. Ge and J. Qian investigated some relations between higher mathematics scores and calculus scores (resp. linear algebra scores, probability statistics scores) for Chinese university students. Based on rough-set theory, they established an information system S = (U,CuD,V, f). In this information system, higher mathematics score was taken as a decision attribute and calculus score, linear algebra score, probability statistics score were taken as condition attributes. They investigated importance of each condition attribute with respective to decision attribute and strength of each condition attribute supporting decision attribute. In this paper, we give further investigations for this issue. Based on the above information system S = (U, CU D, V, f), we analyze the decision rules between condition and decision granules. For each x E U, we obtain support (resp. strength, certainty factor, coverage factor) of the decision rule C —>x D, where C —>x D is the decision rule induced by x in S = (U, CU D, V, f). Results of this paper gives new analysis of on higher mathematics scores for Chinese university students, which can further lead Chinese university students to raise higher mathematics scores in Chinese graduate student entrance examination.<\/p>\r\n","references":"[1] G. Alvatore, M. Bentto and S. Roman, Rough set theory for multi criteria decision analysis, European Journal of Operational Research, 129(2001), 1-47.\r\n[2] Y. Cheng and Y. Ge, Influencing factors with respect to basic essence in national defence for chinese university students, 2008 International Sym\u00acposium on Computer Science and Computational Technology, 2(2008), 528-531.\r\n[3] C. Donna, Artificial interagency research in Japan, Artificial Intelligence, 91(1997), 122-129.\r\n[4] A. Erbert, Scientific discovery and simplicity of method, Artificial Intel-ligence, 91(1997), 177-181.\r\n[5] Y. Ge, Granularity-wise separation in covering approximation spaces, 2008 IEEE International Conference on Granular Computing, 238-243.\r\n[6] R. Golan and W. Ziarko, Methodology for stock market analysis utilizing rough set theory, Proc. of IEEE\/IAFE Conference on Computational Intelligence for Financial Engineering, New Jersey, 22(1995), 32-40.\r\n[7] Z. Pawlak, Rough sets, International Journal of Computer and Information Sciences, 11(1982), 341-356.\r\n[8] Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning About Data, Boston: Kluwer Academic Publishers, 1991.\r\n[9] Z. Pawlak, Rough sets, Communications of ACM, 38(1995), 89-95.\r\n[10] Z. Pawlak, Vagueness and uncertainty - a rough set perspective, Com-putational Intelligence, 11(1995), 227-232.\r\n[11] S. Padmini and H. Donald, Vocabulary mining for information retrieval: rough sets and fuzzy sets, Information Processing and Management, 37(2002), 15-38.\r\n[12] K. Qin, Y. Gao and Z. Pei, On covering rough sets, in RSKT 2007, LNAI, 4481(2007), 34-41.\r\n[13] P. Roger, The Emperor's New Mind: Concerning Computer's Minds and The Laws of Physics, Oxford University Press, 1989, 65-70.\r\n[14] M. Stiefld and S. Smoliar, What computers still can't do: five reviews and a response, Artificial Intelligence, 81(1996), 95-97.\r\n[15] S. Tsumoto, Automated discovery of medical expert system rules from clinical databases based on rough set, Proc. of Second International Conf. on Knowledge discovery and data Mining, USA. 32(1996), 63-72.\r\n[16] M. Yahia, R. Mahmodr and N. Sulaimann, Rough neural expert systems, Expert system with Applications, 18(2002), 87-99.\r\n[17] S. Yang, J. Qian and Y. Ge, Uncertain decision analysis in stocks prices fluctuations, J. of Suzhou University, 24(2008), 6-10.\r\n[18] W. Zhu and F. Wang, On Three types of covering rough sets, IEEE Transactions on Knowledge and Data Engineering, 19(2007), 1131-1144.\r\n","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 35, 2009"}