Computer Science ›› 2017, Vol. 44 ›› Issue (9): 261-265.doi: 10.11896/j.issn.1002-137X.2017.09.049

Previous Articles     Next Articles

Data Consolidation Based on Structure Granulation and Matrix Computation

YAN Lin, GAO Wei and YAN Shuo   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In order to study the problem of data consolidation,and make the consolidation data keep the associated information before consolidation,a structure composed of a data set together with a weighted relation was constructed,which refers to a weighted association structure.It demonstrates a structured representation of various data information.Then by using a granulation set,the weighted association structure is transformed into the weighted granulation structure.This makes the data in a granule to be consolidated according to the granulation information.At the same time,the consolidation data maintain or gather the associated information before consolidation,which leads to a structural granulation method of data consolidation.On this basis,two matrices were introduced,called the weighted association matrix and weighted granulation matrix which are matrix representations of the weighted association structure and weighted granulation structure respectively.Moreover,by matrix computations of elementary transformation and target transformation,the weighted association matrix is transformed into the weighted granulation matrix.This forms an equiva-lent way of data consolidation,and can be taken as the basis of programming algorithm.

Key words: Data consolidation,Granulation set,Weighted association structure,Weighted granulation structure,Weighted association matrix,Weighted granulation matrix

[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!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!