吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 283-290.doi: 10.13229/j.cnki.jdxbgxb201501041

• Orignal Article • Previous Articles     Next Articles

Novel rapid grey incidence analysis method based on data distribution

DAI Jin1,HU Feng2,LIU Xin1   

  1. 1.College of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    2.College of Computer Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2013-12-20 Online:2015-02-01 Published:2015-02-01

Abstract: The classic grey theory does not adequately take into account the distribution of data set, and lacks effective methods to analyze and mine large sample in multi-granularity. Considering the universality of normal distribution, a normality grey number is proposed. Moreover, the corresponding definition and calculation method of the incidence degree between the normality grey numbers are constructed. On this basis, the grey incidence analysis method in multi-granularity is put forward to realize the automatic clustering in the specified granularity without any experience knowledge. Experiments fully demonstrate that the proposed method is effective in knowledge acquisition for large data.

Key words: computer application, grey theory, normal grey number, degree of grey incidence, grey incidence analysis

CLC Number: 

  • TP391
[1] 邓聚龙. 灰理论基础[M]. 武汉: 华中科技大学出版社,2002: 1-46.
[2] Liu S F, Hu M L, Yang Y J. Progress of grey system models[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2012, 29(2): 103-111.
[3] Wei G W. Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information[J]. Expert Systems with Applications, 2011, 38(5): 4824-4828.
[4] Hamzaebi C, Pekkaya M. Determining of stock investments with grey relational analysis [J]. Expert Systems with Applications, 2011, 38(8): 9186-9195.
[5] Wei G W. Gray relational analysis method for intuitionistic fuzzy multiple attribute decision making[J]. Expert Systems with Applications, 2011, 38(9): 11671-11677.
[6] 吴利丰, 刘思峰. 基于灰色凸关联度的面板数据聚类方法及应用[J]. 控制与决策, 2013, 28(7): 1033-1036.
Wu Li-feng, Liu Si-feng. Panel data clustering method based on grey convex relation and its application[J]. Control and Decision, 2013, 28(7): 1033-1036.
[7] 李鹏, 刘思峰. 基于灰色关联分析和 DS 证据理论的区间直觉模糊决策方法[J]. 自动化学报, 2011, 37(8): 993-998.
Li Peng, Liu Si-feng. Interval-valued intuitionistic fuzzy numbers decision-making method based on grey incidence analysis and D-S theory of evidence[J]. Acta Automatica Sinica, 2011, 37(8): 993-998.
[8] 李鹏,刘思峰,方志耕. 基于灰色关联分析和 MYCIN 不确定因子的区间直觉模糊决策方法[J]. 控制与决策, 2012, 27(7): 1009-1014.
Li Peng,Liu Si-feng,Fang Zhi-geng.Interval-valued intuitionistic fuzzy numbers decision-making method based on grey incidence analysis and MYCIN certainty factor[J].Control and Decision,2012,27(7):1009-1014.
[9] 菅利荣, 刘思峰. 面向集合论的灰度定义及灰色粗糙集模型建立[J]. 控制与决策, 2013, 28(5): 721-725.
Jian Li-rong, Liu Si-feng. Definition of grey degree in set theory and construction of grey rough set models[J]. Control and Decision, 2013, 28(5): 721-725.
[10] 王翯华, 朱建军, 方志耕. 基于灰色关联度的多阶段语言评价信息集结方法[J]. 控制与决策, 2013, 28(1): 109-114.
Wang He-hua, Zhu Jian-jun, Fang Zhi-geng. Aggregation of multi-stage linguistic evaluation information based on grey incidence degree[J]. Control and Decision, 2013, 28(1): 109-114.
[11] 刘思峰,党耀国,方志耕,等. 灰色系统理论及其应用[M].5版.北京:科学出版社, 2010.
[12] 张岐山, 秦洪. 灰数灰度的一个新定义[J]. 大庆石油学院学报, 1996, 20(1): 89-92.
Zhang Qi-shan, Qin Hong. New definition of grey number’s grade[J]. Journal of Northeast Petroleum University, 1996, 20(1): 89-92.
[13] Thadewald T, Büning H. Jarque–Bera test and its competitors for testing normality–a power comparison[J]. Journal of Applied Statistics, 2007, 34(1): 87-105.
[14] Lilliefors H W. On the Kolmogorov-Smirnov test for normality with mean and variance unknown[J]. Journal of the American Statistical Association, 1967, 62(318): 399-402.
[15] Box G E R, Cox D R. An analysis of transformations[J]. Journal of the Royal statistical Society: Series B, 1964, 26(2):221-252.
[16] Farnum Nicholas R. Using Johnson curves to describe non-normal process data[J]. Quality Engineering, 1996, 9(2): 329-336.
[17] Bandalos. Reliability generalization of working alliance inventory scale scores[J]. Educational and Psychological Measurement, 2002,62(4): 659-673.
[18] Efron B, Tibshirani R. An Introduction to the Bootstrap[M]. New York: CRC Press, 1993.
[1] LIU Fu,ZONG Yu-xuan,KANG Bing,ZHANG Yi-meng,LIN Cai-xia,ZHAO Hong-wei. Dorsal hand vein recognition system based on optimized texture features [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1844-1850.
[2] WANG Li-min,LIU Yang,SUN Ming-hui,LI Mei-hui. Ensemble of unrestricted K-dependence Bayesian classifiers based on Markov blanket [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1851-1858.
[3] JIN Shun-fu,WANG Bao-shuai,HAO Shan-shan,JIA Xiao-guang,HUO Zhan-qiang. Synchronous sleeping based energy saving strategy of reservation virtual machines in cloud data centers and its performance research [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1859-1866.
[4] ZHAO Dong,SUN Ming-yu,ZHU Jin-long,YU Fan-hua,LIU Guang-jie,CHEN Hui-ling. Improved moth-flame optimization method based on combination of particle swarm optimization and simplex method [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1867-1872.
[5] LIU En-ze,WU Wen-fu. Agricultural surface multiple feature decision fusion disease judgment algorithm based on machine vision [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1873-1878.
[6] OUYANG Dan-tong, FAN Qi. Clause-level context-aware open information extraction [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1563-1570.
[7] LIU Fu, LAN Xu-teng, HOU Tao, KANG Bing, LIU Yun, LIN Cai-xia. Metagenomic clustering method based on k-mer frequency optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1593-1599.
[8] GUI Chun, HUANG Wang-xing. Network clustering method based on improved label propagation algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1600-1605.
[9] LIU Yuan-ning, LIU Shuai, ZHU Xiao-dong, CHEN Yi-hao, ZHENG Shao-ge, SHEN Chun-zhuang. LOG operator and adaptive optimization Gabor filtering for iris recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1606-1613.
[10] CHE Xiang-jiu, WANG Li, GUO Xiao-xin. Improved boundary detection based on multi-scale cues fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1621-1628.
[11] ZHAO Hong-wei, LIU Yu-qi, DONG Li-yan, WANG Yu, LIU Pei. Dynamic route optimization algorithm based on hybrid in ITS [J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12] HUANG Hui, FENG Xi-an, WEI Yan, XU Chi, CHEN Hui-ling. An intelligent system based on enhanced kernel extreme learning machine for choosing the second major [J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[13] FU Wen-bo, ZHANG Jie, CHEN Yong-le. Network topology discovery algorithm against routing spoofing attack in Internet of things [J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[14] CAO Jie, SU Zhe, LI Xiao-xu. Image annotation method based on Corr-LDA model [J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[15] HOU Yong-hong, WANG Li-wei, XING Jia-ming. HTTP-based dynamic adaptive streaming video transmission algorithm [J]. 吉林大学学报(工学版), 2018, 48(4): 1244-1253.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!