绎 210023 - - - doi: 10.11842/wst.2014.06.003 TP274 A 1 6 Zhou X H [6] Yang M [7] [1~3] Fu X J [4] Xiang Y [5] Zhou X Z [8] Zheng G [9] Qiao S J [10] [11] Pawlak 1982 淤 2014-04-11 2014-05-13 Ziarko W [12] 1993 元 SBK2014042320 13XZR36 元元 1222
2014 绎 Vol.16 No.6 Ziarko W 于 2 2.1 K = U R Pawlak R x X Ziarko W [12] 茁 0 臆茁 <0.5 R x 茁 Xiff 逸 1- 茁 X R Pawlak 2.2 [13] Skowron A [14] 1996 Skowron A [15] I=<U C 胰 D V f> A= C 胰 D B A * T 1 T 1 2 I=<U C 胰 D V f> A= C 胰 D X B A X T B X B T 3 3.1 S=<U A V a f a V w f w V c f c > a 沂 A V a a a f a U 寅 V a U a V w U f w U 寅 V w x 沂 U V w f w xv C f C A 伊 V a 寅 V C a v a f a v C a x X 茁 茁沂 [0 0.5[x] R iff 滋 X R x 逸 1- 茁 X R- R- 茁 X 1223
绎 3.2 [16] [17] 1 S=<U A V a f a V w f w V c f c > x i x j C MD ij x i x j ck U D U U/D={D 1 D 2... D m } D c k D x i c x k i 2 S=<U A V a f a V w f w V c f c > c k 沂 C x i x j c k D l x i CS c k c k CS c k c k CS c k 4 [18] 4.1 652 267 652 200 12 652 1 4 4 12 2 1224
2014 绎 Vol.16 No.6 1 4 ={,,,,,,,,,,, } ={} 2 3 4 4 0.59 0.42 0.38 0.38 0.33 0.30 0.30 29 U={29 } C={ } D={} 5 5 [19] 6 5 6 4.2 D ={ } C ={ } 1 1 2 3 4 2 1 0.31 0.23 0.24 0.16 0.25 0.13 0.11 0.10 0.08 0.16 0.03 0.01 2 0.36 0.08 0.12 0.14 0.13 0.08 0.06 0.12 0.09 0.08 0.04 0.03 3 0.13 0.10 0.12 0.18 0.15 0.14 0.11 0.07 0.08 0.02 0.03 0 4 0.52 0.34 0.32 0.13 0.15 0.11 0.08 0.23 0.08 0.04 0.09 0.03 0.39 0.25 0.25 0.19 0.18 0.12 0.17 0.11 0.08 0.08 0.06 0.02 3 0.370 0.242 0.205 0.171 0.152 0.113 0.101 0.092 0.085 0.023 0.011 0.01 1225
绎 5 6 5 6 淤 于 - - 盂 - - - - 榆 虞 - - 愚 4 1 0.59 15 0.19 2 0.42 16 0.17 3 0.38 17 0.15 4 0.38 18 0.15 5 0.33 19 0.14 6 0.30 20 0.14 7 0.30 21 0.13 8 0.28 22 0.11 9 0.27 23 0.11 10 0.26 24 0.11 11 0.25 25 0.10 12 0.24 26 0.10 13 0.21 27 0.10 14 0.21 28 0.08 5 1 0.478 10 0.120 2 0.425 11 0.130 3 0.384 12 0.130 4 0.371 13 0.087 5 0.217 14 0.087 6 0.267 15 0.087 7 0.304 16 0.044 8 0.217 17 0.044 9 0.187 6 0.452 6 0.443 0.369 0.209 0.338 0.270 0.225 1226
2014 绎 Vol.16 No.6 5 Conf=> [ <=>] Conf=> Corr[] 1 0.52 <=> 0.28 3.15 2 0.31 <=> 0.35 3.06 3 0.33 <=> 0.32 2.81 4 0.34 <=> 0.31 2.77 5 0.45 <=> 0.57 2.23 6 0.29 <=> 0.56 2.2 7 0.36 <=> 0.55 2.15 8 0.36 <=> 0.51 2.00 9 0.32 <=> 0.44 1.97 10 0.32 <=> 0.40 1.57 11 0.28 <=> 0.65 1.36 12 0.48 <=> 0.30 1.36 13 0.39 <=> 0.63 1.32 14 0.58 <=> 0.33 1.27 15 0.28 <=> 0.28 1.27 16 0.28 <=> 0.28 1.27 17 0.57 <=> 0.28 1.24 18 0.36 <=> 0.30 1.23 19 0.3 <=> 0.56 1.13 20 0.35 <=> 0.62 1.08 21 0.31 <=> 0.53 1.07 22 0.34 <=> 0.53 1.07 23 0.33 <=> 0.50 1.05 24 0.3 <=> 0.59 1.04 6 - Conf=> [<=>] Conf=> Corr 1 0.33 <=> 0.30 3.15 2 0.32 <=> 0.33 3.10 3 0.31 <=> 0.30 3.08 4 0.29 <=> 0.35 2.92 5 0.33 <=> 0.28 2.69 6 0.28 <=> 0.32 2.67 7 0.32 <=> 0.30 2.46 8 0.34 <=> 0.40 2.11 9 0.35 <=> 0.41 2.11 10 0.30 <=> 0.28 2.09 11 0.33 <=> 0.34 2.07 12 0.31 <=> 0.29 1.94 13 0.3 <=> 0.39 1.47 14 0.32 <=> 0.35 1.3 15 0.28 <=> 0.33 1.23 16 0.43 <=> 0.36 1 5 3 淤 于 盂 1 Swans on D, Smalheiser N R. An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artif Intell,1997, 91(2) 颐 183~203. 2 Hristovski D, Peterlin B, Mitchell J A, et al. Using literature -based discovery to identify disease candidate genes. Int J Med Inform, 2005, 74 (2/4) 颐 289~298. 3 Hettne K M, de Mos M, de Bruijn A G, et al. Applied information retrieval and multidisciplinary research: new mechanistic hypotheses in complex regional pain syndrome. J Biomed Discov Collab, 2007, 2(2) 颐 1~16. 4 Fu X J, Song X X, Wei L B, et al. 1227
绎 Study of the distribution patterns of the constituent herbs in classical Chinese medicine prescriptions treating respiratory disease by data mining methods. Chin J Integ Med, 2013, 19(8) 颐 621~628. 5 Xiang Y, Yu J W, Cheng Y B, et al. Study on composing prescription laws of treating aplastic anemia by Chinese medicine using applying data mining technique. Chin J Integ Trad West Med, 2013, 33(7) 颐 906~910. 6 Zhou X H, Li S L, Tian F, et al. Building a disease risk model of osteoporosis based on traditional Chinese medicine symptoms and western medicine risk factors. Stat Med, 2012, 31(7) 颐 643~652. 7 Yang M, Jiao L J, Chen P Q, et al. Complex systems entropy network and its application in data mining for Chinese medicine tumor clinics. WST, 2012, 14(2) 颐 1376~1384. 8 Zhou X Z, Zhang R S, Shah J, et al. Patterns of herbal combination for the treatment of insomnia commonly employed by highly experienced Chinese medicine physicians. Chin J Integ Med, 2011, 17(9) 颐 655~662. 9 Zheng G, Jiang M, He X J, et al. Discrete derivative: a data slicing algorithm for exploration of sharing biological networks between rheumatoid arthritis and coronary heart disease. Bio Data Mining, 2011, 4 颐 18. 10 Qiao S J, Tang C J, Jin H D, et al. KISTCM: knowledge discovery system for traditional Chinese medicine. Appl Intell, 2010, 32(3) 颐 346~363. 11 Pawlak Z. Rough sets. IJICS, 1982, 11(5) 颐 341~356. 12 Ziarko W. Variable precision rough set model. J Comput Syst Sci, 1993, 46(1) 颐 39~59. 13,,. Generalization rough set theory. Journal of Donghua University (English Edition), 2008, 25(6) 颐 654~658. 14 Skowron A. Tolerance approximation spaces. Fundamenta Informaticae, 1996, 27(2/3) 颐 245~253. 15 Meng Z Q, Shi Z Z. A fast approach to attribute reduction in incomplete decision systems with tolerance relation -based rough sets. Information Sciences, 2009, 179(16) 颐 2774~2793. 16..,2012,48(35) 颐 110~113,117. 17,.. (),2013(1) 颐 42~46. 18..:,2005 颐 378~48691. 19..:,1998 颐 16~17. Research on Chinese Prescription Compatibility Based on Variable Precision Tolerance Model and Attribute Sensitivity Reduction She Kankan, Hu Kongfa, Wang Zhen (Department of Information Technology, Nanjing University of Traditional Chinese Medicine, Nanjing 210023, China) Abstract: Rough set theory is a powerful tool to deal with incomplete information system, which can be applied to prescription data analysis. In this paper, we suggested an improved rough set model called WVP -T model. The model combined the variable precision model with the tolerance relation model. It can overcome the shortcoming of classical model. Furthermore, attribute importance and entropy of information were combined as heuristic information. Medicine was mapped to rough set attribute in order to value its importance. Then, combined with curative effect, attribute reduction was used to investigate the relationship between prescription and medicine and the relationship between symptom and syndrome. The experimental results showed that algorithm proposed in this paper can be used in prescription data analysis and can accurately reveal the compatibility rules to guide the clinical medication. Keywords: Rough sets, tolerance relation, attribute reduction, prescription compatibility, correspondence of prescription and syndrome 1228