Computer Science ›› 2019, Vol. 46 ›› Issue (6): 224-230.doi: 10.11896/j.issn.1002-137X.2019.06.034

Previous Articles     Next Articles

Knowledge Discovery Model Based on Neighborhood Multi-granularity Rough Sets

CHENG Yi1,2, LIU Yong3   

  1. (College of Computer Science,Sichuan University,Chengdu 610000,China)1
    (Department of Information and Engineering,Sichuan College of Architectural Technology,Chengdu 610000,China)2
    (Department of Electrical Engineering,Sichuan College of Architectural Technology,Deyang,Sichuan 618000,China)3
  • Received:2018-05-14 Published:2019-06-24

Abstract: It is the purpose of the present work to re-establish a knowledge discovery model based on neighborhood multi-granulation rough sets from the perspective of the deficiency with respect to the existing definition of neighborhood multi-granulation rough sets and the corresponding knowledge discovery algorithms.We firstly constructed the optimistic neighborhood multi-granulation rough set model and pessimistic neighborhood multi-granulation rough set model under multiple neighborhood radii,and discussed several pertinent properties.Then we gave a definition for the granularity importance of neighborhood multi-granulation rough sets,and constructed a granularity reduction algorithm.Finally we conducted a demonstration for the acting mechanism of the proposed algorithm by using an example,and veri-fied its validity.

Key words: Multi-granulation, Neighborhood, Rough sets

CLC Number: 

  • TP182
[1]LIN T Y,LIU Q,HUANG K J,et al.Rough sets neighborhood systems and approximation[C]∥Proc of the 5th International Symposium on Methodologies of Intelligent Systems.Knoxville,1990:130-141.
[2]QIAN Y H,LIANG J Y.Rough set method based on multi-granulations[C]∥Proceedings of 5th IEEE Conference on Cognitive Informatics.New York:IEEE,2006:297-304.
[3]QIAN Y H,LIANG J Y,YAO Y Y,et al.MGRS:A multigranulation rough set[J].Information Sciences,2010,180(6):949-970.
[4]QIAN Y H,LIANG J Y,DANG C Y.Incomplete multigranulation rough set[J].IEEE Transation on Systems,Man and Cybernetics,2010,40(2):420-431.
[5]QIAN Y H,ZHANG H,SANG Y L,et al.Multigranulation decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2014,55(1):225-237.
[6]LIN G P,QIAN Y H,LI J J.NMGRS:Neighborhood-based multigranulation rough set[J].International Journal of Approxi-mate Reasoning,2012,53(7):1080-1093.
[7]YANG X B,SONG X N,DOU H L,et al.Multi-granulation rough set:from crisp to fuzzy case[J].Annals of Fuzzy Mathematics and Informatics,2011,1(1):55-70.
[8]XU W H,WANG Q R,ZHANG X T.Multi-granulation rough sets based on tolerance relations[J].Soft Compute,2013,17(7):1241-1252.
[9]LIU C H,MIAO D Q.On multi-granulation covering rough sets[J].International Journal of Approximate Reasoning,2014,55(6):1404-1418.
[10]SHE Y H,HE X L,SHI H X,et al.A multiple-valued logic approach for multigranulation rough set model[J].International Journal of Approximate Reasoning,2017,82:270-284.
[11]YAO Y Y,SHE Y H.Rough set models in multigranulation spaces[J].Information Sciences,2016,327:40-56.
[12]SUN B Z,MA W M,QIAN Y H.Multigranulation fuzzy rough set over two universes and its application to decision making[J].Knowledge-Based Systems,2017,123:61-74.
[13]SHE Y H,HE X L,SHI H X,et al.A multiple-valued logic approach for multigranulation rough set model[J].International Journal of Approximate Reasoning,2017,82:270-284.
[14]QIAN Y H,LIANG X Y,LIN G P,et al.Local multigranulation decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2017,82:119-137.
[15]FENG T,MI J S.Variable precision multigranulation decision-theoretic fuzzy rough sets[J].Knowledge-Based Systems,2016,91:93-101.
[16]XU Y,YANG H J,JI X.Neighborhood multi-granulation rough set model based on double granulate criterion[J].Control and Decision,2015,30(8):1469-1478.(in Chinese)
徐怡,杨宏健,纪霞.基于双重粒化准则的邻域多粒度粗糙集模型[J].控制与决策,2015,30(8):1469-1478.
[17]MA F M,CHEN J W,ZHANG T F.Quick attribute reduction algorithm for neighbor-hood multi-granulation rough set based on double granulate criterion[J].Control and Decision,2017,32(6):1121-1127.(in Chinese)
马福民,陈静雯,张腾飞.基于双重粒化准则的邻域多粒度粗集快速约简算法[J].控制与决策,2017,32(6):1121-1127.
[1] WANG Jie, LI Xiao-nan, LI Guan-yu. Adaptive Attention-based Knowledge Graph Completion [J]. Computer Science, 2022, 49(7): 204-211.
[2] XU Si-yu, QIN Ke-yun. Topological Properties of Fuzzy Rough Sets Based on Residuated Lattices [J]. Computer Science, 2022, 49(6A): 140-143.
[3] TAN Ren-shen, XU Long-bo, ZHOU Bing, JING Zhao-xia, HUANG Xiang-sheng. Optimization and Simulation of General Operation and Maintenance Path Planning Model for Offshore Wind Farms [J]. Computer Science, 2022, 49(6A): 795-801.
[4] FANG Lian-hua, LIN Yu-mei, WU Wei-zhi. Optimal Scale Selection in Random Multi-scale Ordered Decision Systems [J]. Computer Science, 2022, 49(6): 172-179.
[5] CHEN Yu-si, AI Zhi-hua, ZHANG Qing-hua. Efficient Neighborhood Covering Model Based on Triangle Inequality Checkand Local Strategy [J]. Computer Science, 2022, 49(5): 152-158.
[6] SUN Lin, HUANG Miao-miao, XU Jiu-cheng. Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief [J]. Computer Science, 2022, 49(4): 152-160.
[7] XUE Zhan-ao, HOU Hao-dong, SUN Bing-xin, YAO Shou-qian. Label-based Approach for Dynamic Updating Approximations in Incomplete Fuzzy Probabilistic Rough Sets over Two Universes [J]. Computer Science, 2022, 49(3): 255-262.
[8] LIU Yi, MAO Ying-chi, CHENG Yang-kun, GAO Jian, WANG Long-bao. Locality and Consistency Based Sequential Ensemble Method for Outlier Detection [J]. Computer Science, 2022, 49(1): 146-152.
[9] SHI Ke-xiang, BAO Li-yong, DING Hong-wei, GUAN Zheng, ZHAO Lei. Chaos Artificial Bee Colony Algorithm Based on Homogenizing Optimization of Generated Time Series [J]. Computer Science, 2021, 48(7): 270-280.
[10] XUE Zhan-ao, SUN Bing-xin, HOU Hao-dong, JING Meng-meng. Optimal Granulation Selection Method Based on Multi-granulation Rough Intuitionistic Hesitant Fuzzy Sets [J]. Computer Science, 2021, 48(10): 98-106.
[11] XUE Zhan-ao, ZHANG Min, ZHAO Li-ping, LI Yong-xiang. Variable Three-way Decision Model of Multi-granulation Decision Rough Sets Under Set-pair Dominance Relation [J]. Computer Science, 2021, 48(1): 157-166.
[12] CHEN Yu-jin, XU Ji-hui, SHI Jia-hui, LIU Yu. Three-way Decision Models Based on Intuitionistic Hesitant Fuzzy Sets and Its Applications [J]. Computer Science, 2020, 47(8): 144-150.
[13] ZHOU Jun-li, GUAN Yan-yong, XU Fa-sheng and WANG Hong-kai. Core in Covering Approximation Space and Its Properties [J]. Computer Science, 2020, 47(6A): 526-529.
[14] WANG Yi-rou,ZHANG Da-min,XU Hang,SONG Ting-ting,FAN Ying. Spectrum Allocation Strategy for Neighborhood Network Based Cognitive Smart Grid [J]. Computer Science, 2020, 47(3): 267-272.
[15] YANG Jie,WANG Guo-yin,LI Shuai. Neighborhood Knowledge Distance Measure Model Based on Boundary Regions [J]. Computer Science, 2020, 47(3): 61-66.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!