Knowledge Induction Ba sed on Genera liza tion of M ulti2benchmark A ttr ibute

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1 39 2 ( ) Vol. 39 No JOURNAL OF SICHUAN UN IVERSITY ( ENGINEER ING SC IENCE ED ITION) Mar : (2007) , 2, 1, 1, 3, 4, 1, 5 (1., ; 2., ; 3., ; 4., ; 5., ) :,,, 27% : ; ;; : TP311 : A Knowledge Induction Ba sed on Genera liza tion of M ulti2benchmark A ttr ibute L IU Q i2hong 1, 2, TAN G Chang2jie 1, L I Chuan 1, ZHU Jun 3, L IU Q i2w ei 4, Q IAO Shao2jie 1, J IAN G Yong2guang 5 (1. School of Computer Sci., Sichuan Univ., Chengdu , China; 2. Electrical Eng. and Info., Sichuan Univ., Chengdu , China; 3. B irth D isfigurement Surveillance Center of China, Sichuan Univ., Chengdu , China; 4. School of Public Administration, Sichuan Univ., Chengdu , China; 5. Chengdu Univ. of Traditional Chinese Medicine, Chengdu , China) Abstract: Through analyzing the disfigurem ent of basic attribute2oriented induction arithmetic, a new op tim ized technic and arithmetic based on generalizing hiberarchy of relevancy attribute2oriented was p roposed for Chinese medicine p rescrip tion data m ining. The efficiency of induction and the accuracy of discovery know ledge by the per2 tinency of relevancy2attribute2threshold and benchmark2attribute2threshold were enhanced, and generalizing hiberar2 chy and trees for relevancy2attribute based on Chinese medical know ledge were established. A new system about Chinese medicine p rescrip tion know ledge discovery and analysis was imp lemented. The effectivity of the new arith2 metic was p roved by four extensive experiments. The efficiency was heighten above 27% comparing w ith the con2 ventional arithm etic in the same condition. The speciality know ledge and rules in accordance w ith Chinese medicine theory can be found more rap idly by the new system. Key words: data m ining; relevancy2attribute2oriented induction; threshold of multi2benchmark2attribute ; know l2 edge discovery of Chinese herbal m edicine : : ( ; ) ; ( 2006Z ) ; (2006BA I05A001) :( ),,,. :.

2 2, : 117,,,, [ 1-2 ] AO I [ 3-4 ],, / T_AO I MT _AO I : 1) ; 2) ; 3),, ; 4), 27%, 2),, [ 7-8 ],,,,,, 1 ( 1) 1,, : 1), any; 2) () ; 1 [ 5-6 ],: 1), [ 5-6 ] ; 1 Tab. 1 The pr im ord ia l da ta of Ch inese m ed ic ine prescr iption 1 F ig. 1 Concept tree about a ttr ibute of m ed icam en t eff icacy 101,,,, ,, ( ) ,,,,,,13. 75, 110

3 118 ( ) 39 3) ; 4), ;, 2 /, : 1 T 6 (V, E, I, O, L, H),, V = { v 1, v 2,, v n }, E = { e ij v i, v j V }, I, O, L, H 12 2 F ig. 2 Concept tree about a ttr ibute of dyna sty, k, 0, 1,, n - 1, k k, H, 4, 4,,, 2 ( ) 1, 2,, N, k N,H 3 (Concepts, 4, k),, Concepts, 4 Concepts, k,,,,, : 3 R ( A 1, A 2,, A n, B m ), B m A n, A n, B m ; A n,b m 4 ( ) R ( A 1, A 2,, A n, B m ) A n, B m D i, T i A n, T j B m a i, a i [0, 1 ]; C i, C i [1, 20 ]; f( f = 0, n < 0; f = n, 0 < n < 1; f = 1, n > 1) 1) T j = 6 n i =1 f ( a i T i ) + C i, B m, ; 2) T j = f ( a i T i ) + C i, B m, 54 M T = ( l, f l, T i, T j ),, l = ( c 1, c 2,, c n ) R, R, f l ( ) = { ( a 1, a 2,, a m ) a i Φ c i, i = 1, 2,, m } s, Φ H ; T i, T j,, [ 8 ],, 3, T_AO I( Threshold of single benchmark AO I) M T_AO I (M ulti2benchmark2attribute2threshold AO I) : 1) ; 2), ; 3) ; 4),

4 2, : 119 ; 5), ; 6 ),,, : 1 / / MT_ AO I(M ulti2benchm ark2threshold AO I) : 1) DB; 2) DMQuery; 3)A i T i : R Step 1. M Get_ p rescrip tion_data ( ) / / M ; Step 2. 1 ) T j = f( 6 n a i T i ) + C i a i, b j c i,t j i = 1 ; 2) CT L i R i, ; Step 3. Prepare_for_generalition ( ) / /M : 1) M, A i [A i ], [A i ] T i, CT H i ; 2) T j, CT H i, Step 4. R Generalition ( ) / / M, D, M,, R i,r, R 2 / / T_ AO I( Threshold of single benchmark AO I),,,, 3 : 1 ) R ; 2 ) DMQuery; 3) : Step 1. R Sorting ( ) / /R R,,, Step 2. 4 : Intel Pentinum 4 CPU: 1. 4 GHz, 1 G; VC MS SQLSERVER2000 W INGDOW S2000; : DB DB 22,,,,,,, 116 Formula, 201 Syndrome, 845Symp tom 3 T j = a i T i + C i,2 ( ),, 3 Fig. 3 Part of rules and results of experiments and discovery

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