计算机科学 ›› 2017, Vol. 44 ›› Issue (9): 261-265.doi: 10.11896/j.issn.1002-137X.2017.09.049
闫林,高伟,闫硕
YAN Lin, GAO Wei and YAN Shuo
摘要: 为了研究数据合并问题,并使合并数据保持合并前的数据之间的关联关系,对各类数据信息给予了结构化的表示,对应产生了由数据集和加权关系组合构成的加权关联结构;进而通过数据集的合并粒化集,完成了加权关联结构向加权粒化结构的转换,使数据集中的数据依据粒化信息得到了合并,并保持或汇集了合并前的数据之间的关联信息,由此形成了数据合并的结构粒化方法。在此基础上,构建了加权关联矩阵和加权粒化矩阵,分别作为加权关联结构和加权粒化结构的矩阵表示。经中间变换和目标变换的矩阵计算,实现了加权关联矩阵向加权粒化矩阵的变换,产生了与结构粒化等价的矩阵变换方法,形成了程序设计的算法基础。
[1] ZHANG J B,LI T R,CHEN H M.Composite rough sets for dynamic data mining[J].Information Sciences,2014,257:81-100. [2] ZHANG J B,LI T R,RUAN D.Neighborhood rough sets for dynamic data mining[J].International Journal of Intelligent Systems,2012,27(4):317-342. [3] HONKO P.Association discovery from relational data via granu-lar computing[J].Information Sciences,2013,234(11):136-149. [4] MERIGO J M.The probabilistic weighted average and its application in multiperson decision making[J].International Journal of Intelligent Systems,2012,27(5):457-476. [5] BEAUBOUEF T,PETRY F.Fuzzy rough set techniques for uncertainty processing in a relational database[J].International Journal of Intelligent System,2000,15(5):389-424. [6] BEAUBOUEF T,PETRY F,ARORA G.Information-theoretic measures of uncertainty for rough sets and rough relational database[J].Information Sciences,1998,109(1-4):185-195. [7] COZMAN F G.Independence for full conditional probabilities:structure,factorization,non-uniqueness,and bayesian networks [J].International Journal of Approximate Reasoning,2013,54(9):1261-1278. [8] TAGARELLI A.Exploring dictionary-based semantic related-ness in labeled tree data[J].Information Sciences,2013,220(1):244-268. [9] SHE Y L.On the rough consistency measures of logic theories and approximate reasoning in rough logic[J].International Journal of Approximate Reasoning,2014,55(1):486-499. [10] YAN S,YAN L,WU J Z.Rough data-deduction based on the upper approximation [J].Information Sciences,2016,373:308-320. [11] YAN L,YAN S.Granular reasoning and decision systems de-composition [J].Journal of Software,2012,7(3):683-690. [12] YAN L,YAN S.Researches on rough truth of rough axiomsbased on granular reasoning[J].Journal of Software,2014,9(2):265-273. [13] LI J H,MEI C L,LV Y J.Incomplete decision contexts:approxi-mate concept construction,rule acquisition and knowledge reduction [J].International Journal of Approximate Reasoning,2013,54(1):149-165. [14] JIA X Y,LIAO W H,TANG Z M.Minimum cost attribute reduction in decision-theoretic rough set models [J].Information Sciences,2013,9(1):151-167. [15] MCALLISTER R A,ANGRYK R A.Abstracting for dimen-sionality reduction in text classification [J].International Journal of Intelligent Systems,2013,28(2):115-138. [16] 闫林.数理逻辑基础与粒计算[M].北京:科学出版社,2007. [17] PEDRYCZ W.Granular computing:analysis and design of intelligent systems [M] .Boca Raton,USA:CRC Press Francis Taylor,2013. [18] LI J H,MEI C L,XU W H,et al.Concept learning via granular computing:A cognitive viewpoint[J].Information Sciences,2015,298(1):447-467. [19] YAN L,LIU T,YAN S,et al.Data combination method based on structure’s granulation[J].Journal of Computer Applications,2015,35(2):358-363.(in Chinese) 闫林,刘涛,闫硕,等.基于结构粒化的数据合并方法[J].计算机应用,2015,35(2):358-363. |
No related articles found! |
|