[1] WANG Guoyin,ZHANG Qinghua,HU Jun.An overview of granular computing[J].CAAI Transactions on Intelligent Systems,2007,12(6):8-26.(in Chinese)王国胤,张清华,胡军.粒计算研究综述[J].智能系统学报,2007,12(6):8-26. [2] MIAO Duoqian,WANG Guoyin,LIU Qing.Granular computing:past,present and future[M].Beijing:Science Press,2007.(in Chinese)苗夺谦,王国胤,刘清.粒计算:过去、现在与展望[M].北京:科学出版社,2007. [3] ZHANG Fengwang.Application of SVM based on infor-mation granulation in securities time series analysis[D].Kunming:Kunming University of Science and Technology,2014.(in Chinese)张丰旺.基于信息粒化的SVM在证券时间序列分析中的应用[D].昆明:昆明理工大学,2014. [4] PEDRYCZ W,AL-HMOUZ R,MORFEQ A,et al.The design of free structure granular mappings:the use of the principle of justifiable granularity[J].IEEE Transactions on Cybernetics,2013,43(6):2105-2113. [5] ZHU X,PEDRYCZ W,LI Z.Granular description of data:building information granules with the aid of the principle of justifiable granularity[C]//Proceedings of 2016 IEEE International Conference on Fuzzy Systems.Washington D.C.,USA:IEEE Press,2016:969-976. [6] AL-HMOUZ R,PEDRYCZ W,BALAMASH A,et al.From data to granular data and granular classifiers[C]//Proceedings of 2014 IEEE International Conference on Fuzzy Systems.Washington D.C.,USA:IEEE Press,2014:432-438. [7] GACEK A,PEDRYCZ W.Clustering granular data and their characterization with information granules of higher type[J].IEEE Transactions on Fuzzy Systems,2015,23(4):850-860. [8] EFFATI S,SADOGHI H,YAZDI A J.Fuzzy clustering algorithm for fuzzy data based on α-cuts[M].[S.l.]:IOS Press,2013. [9] PETER G,LINGRAS P.Rough sets:selected methods and applications in management and engineering[M].Berlin,Germany:Springer,2012. [10] HU Qinghua,YU Daren.An improved clustering algorithm for information granulation[C]//Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery.Berlin,Germany:Springer,2005:494-504. [11] HWANG C,RHEE C H.Uncertain fuzzy clustering:interval type-2 fuzzy approach to C-Means[J].IEEE Transactions on Fuzzy Systems,2007,15(1):107-120. [12] YU Long,XIAO Jian,ZHOU Cong.Robust interval type-2 possibilistic C-Means clustering[J].Control and Decision,2009,24(4):503-507. [13] RUBIO E,CASTILLO O,MELIN P.Interval type-2 fuzzy system design based on the interval type-2 fuzzy C-Means algorithm[M]//COLLAN M,FEDRIZZI M,KACPRZYK J.Fuzzy technology.Berlin,Germany:Springer,2016. [14] LU Ruiqiang.Rough clustering and granulation analysis for uncertain information and its application[D].Nanjing:Nanjing University of Finance & Economics,2018.(in Chinese)逯瑞强.不确定信息的粗糙聚类与粒化分析及应用[D].南京:南京财经大学,2018. [15] PEDRYCZ W.The principle of justifiable granularity and an optimization of information granularity allocation as fundamentals of granular computing[J].Journal of Information Processing Systems,2011,7(3):397-412. [16] PEDRYCZ W,SUCCI G,SILLITTI A,et al.Data description:a general framework of information granules[J].Knowledge-Based Systems,2015,80:98-108. [17] ZHONG C,PEDRYCZ W,WANG D,et al.Granular data imputation:a framework of granular computing[J].Applied Soft Computing,2016,46:307-316. [18] WANG D,PEDRYCZ W,LI Z.Design of granular interval-valued information granules with the use of the principle of justifiable granularity and their applications to system modeling of higher type[J].Soft Computing,2016,20(6):2119-2134. [19] SHEN Y,PEDRYCZ W,WANG X.Clustering homogeneous granular data:formation and evaluation[J].IEEE Transactions on Cybernetics,2019,49(4):1391-1402. [20] WANG D,PEDRYCZ W,LI Z.Granular data aggregation:an adaptive principle of the justifiable granularity approach[J].IEEE Transactions on Cybernetics,2018,49(2):1-10. [21] PEDRYCZ W,WANG X.Designing fuzzy sets with the use of the parametric principle of justifiable granularity[J].IEEE Transactions on Fuzzy Systems,2016,24(2):489-496. [22] LIU S,PEDRYCZ W,GACEK A,et al.A two-phase method of forming a granular representation of signals[J].Signal Processing,2017,141:1-15. [23] WANG X,PEDRYCZ W,GACEK A,et al.From numeric data to information granules:a design through clustering and the principle of justifiable granularity[J].Knowledge-Based Systems,2016,101:100-113. [24] FU C,LU W,PEDRYCZ W,et al.Fuzzy granular classifica-tion based on the principle of justifiable granularity[J].Knowledge-Based Systems,2019,170:89-101. [25] PEDRYCZ W,HOMENDA W.Building the fundamentals of granular computing:a principle of justifiable granularity[J].Applied Soft Computing,2013,13(10):4209-4218. |