基于粒划分粒重叠发现原理的特征选择方法研究
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摘要
粒计算是一种基于问题概念空间划分的新的智能计算理论和方法；粒计算能够有效分析和处理模糊、不精确、不一致和部分真值的问题；粒计算是覆盖了所有有关粒度的理论、方法论、技术和工具的研究；粒计算不仅是模糊集理论、粗糙集理论、商空间理论等的超集，而且已经成为人工智能领域中一个重要的研究分支，具有广泛的的应用前景。
首先，本文基于粒计算理论和形式概念分析理论，讨论了粒计算理论中有关粒、粒度、粒层等基本概念，以及形式概念分析理论中有关偏序、形式背景等的基本概念。基于中医证名、证素所形成的形势背景，进行了“粒”和“粒空间”的构造，以此来作为概念格的化简。最终，在“粒”和“粒空间”构造的基础之上形成一种满足人们需求的、背景复杂的、层次清晰的、化简简单的基于粒计算的属性约简方法，此方法所形成的粒具有层次关系。比如：对于属性投影空间，最细的是由M决定的粒空间，这时每一个概念就是一个粒；最粗的是由(?)决定的粒空间，这时所有概念是一个粒。
其次，论文提出了一种新的形势背景优化的方法、属性偏序结构图构造的方法，形成了无交叉、层次清晰明了的粒层粗细的概念格粒度划分图。
最后，论文依据有关肾病证类的实际事例，进行了属性偏序结构图的构造。换句话说，就是进行了粒度层次的划分或者等价关系的构造，以便于进行肾病证类的确诊或者规则的提取。
Granular Computing is the new concept and computing model to informationproceeding, and then the main idea is problem solving on different granualr hierarchies;Granular Computing is theory which can effectively analyse and solve fuzzy, imprecise,inconsistent, partial true; Granular Computing covers all theories, methodologies,techniques, and tools which make use of granules. Granular Computing is not only thesuperset of fuzzy set theroy, rough set theroy, quotient space theroy, but also have becomean important research branch of artifical intelligence field. And granular computing has abroad application prospects.
First, in the theory of granular computing and formal concept analysis theory, thepaper discusses the basic concepts related to granular, granularity, granulation, partialorder and formal context. In the formal context of Traditional Chinese Medicine syndrome,syndrome elements, people can construct granular and granular space in order to simplifythe concept lattice. According to the structure of granular and granular space, the paperdisscusses an attribute reduction method based on Granular Computing which not onlycan meet people’s needs, but aslo hold complicated background, clear level, simplification.By this method granular has become a hierarchical relationship. For example, forattributes projective space, the finest granular space is decided by attribute M , and everyconcept is a granular; The thickest granular space is made up of attribute , and all of theconcepts is a granular.
Second, the paper presents a new method which is related to the optimization of theformal context and the construction of the attribute partial order graph. In order to conducta granularity devision graph which is no cross, clear level and have thick or finegranularity.
Finally, the paper is based on the actual examples of nephrotic syndrome to constructattribute partial order graph. In other words, attribute partial order graph reflects thehierarchical of the granularity and the construction of equivalence relations, forconducting the diagnose of the nephrotic syndrome or rule extraction.
首先，本文基于粒计算理论和形式概念分析理论，讨论了粒计算理论中有关粒、粒度、粒层等基本概念，以及形式概念分析理论中有关偏序、形式背景等的基本概念。基于中医证名、证素所形成的形势背景，进行了“粒”和“粒空间”的构造，以此来作为概念格的化简。最终，在“粒”和“粒空间”构造的基础之上形成一种满足人们需求的、背景复杂的、层次清晰的、化简简单的基于粒计算的属性约简方法，此方法所形成的粒具有层次关系。比如：对于属性投影空间，最细的是由M决定的粒空间，这时每一个概念就是一个粒；最粗的是由(?)决定的粒空间，这时所有概念是一个粒。
其次，论文提出了一种新的形势背景优化的方法、属性偏序结构图构造的方法，形成了无交叉、层次清晰明了的粒层粗细的概念格粒度划分图。
最后，论文依据有关肾病证类的实际事例，进行了属性偏序结构图的构造。换句话说，就是进行了粒度层次的划分或者等价关系的构造，以便于进行肾病证类的确诊或者规则的提取。
Granular Computing is the new concept and computing model to informationproceeding, and then the main idea is problem solving on different granualr hierarchies;Granular Computing is theory which can effectively analyse and solve fuzzy, imprecise,inconsistent, partial true; Granular Computing covers all theories, methodologies,techniques, and tools which make use of granules. Granular Computing is not only thesuperset of fuzzy set theroy, rough set theroy, quotient space theroy, but also have becomean important research branch of artifical intelligence field. And granular computing has abroad application prospects.
First, in the theory of granular computing and formal concept analysis theory, thepaper discusses the basic concepts related to granular, granularity, granulation, partialorder and formal context. In the formal context of Traditional Chinese Medicine syndrome,syndrome elements, people can construct granular and granular space in order to simplifythe concept lattice. According to the structure of granular and granular space, the paperdisscusses an attribute reduction method based on Granular Computing which not onlycan meet people’s needs, but aslo hold complicated background, clear level, simplification.By this method granular has become a hierarchical relationship. For example, forattributes projective space, the finest granular space is decided by attribute M , and everyconcept is a granular; The thickest granular space is made up of attribute , and all of theconcepts is a granular.
Second, the paper presents a new method which is related to the optimization of theformal context and the construction of the attribute partial order graph. In order to conducta granularity devision graph which is no cross, clear level and have thick or finegranularity.
Finally, the paper is based on the actual examples of nephrotic syndrome to constructattribute partial order graph. In other words, attribute partial order graph reflects thehierarchical of the granularity and the construction of equivalence relations, forconducting the diagnose of the nephrotic syndrome or rule extraction.
引文
[1]李雄飞,李军.数据挖掘与知识发现[M].北京:高等教育出版社, 2003: 9-70.
[2]唐笑林.数据挖掘技术的研究和应用[J].华东理工大学学报(自然科学版), 2008, 34(2):290-295.
[3] Mallach G E. Decision Support and Data Warehouse Systems[M]. Beijing: Publishing House ofElectronics Industry, 2001: 414-415.
[4] Studer R, Benjamins V R, Fensel D. Knowledge engineering, principles and methods[J]. Data andKnowledge Engineering, 1998, 25(22): 161-197.
[5]李善平.本体论研究综述[J].计算机研究与发展, 2004, 41(7): 1041-1052.
[6]刘清,孙辉,王洪发.粒计算研究现状及基于粗糙集逻辑语义的粒计算研究[J].计算机学报,2008, 31(4): 543-555.
[7]华蓓,钟诚.数据挖掘中的隐私保护技术进展分析[J].微电子学与计算机, 2007, 26(8):38-41.
[8]王国胤,张清华,胡军.粒计算研究综述[J].智能系统学报, 2007, 2(6): 8-26.
[9]谢克明,逯新红,陈泽华.粒计算的基本问题和研究[J].计算机工程与应用, 2007, 43(16):41-44.
[10]李道国,苗夺谦,张红云.粒度计算的理论、模型与方法[J].复旦学报, 2004, 43(5): 837-841.
[11]王飞跃.词计算和语言动力学系统的计算理论框架[J].模式识别与人工智能, 2001, 14(4):371-384.
[12]陈欢.基于粗糙集理论的值约简及规则提取[J].福州大学学报, 2004, 32(4): 472-475.
[13]刘清,孙辉.从粗糙集的发展前景看粒计算的研究趋势[J].南昌工程学院学报, 2006, 5(25):1-10.
[14]张文修,吴志伟,梁吉业,等.粗糙集理论与方法[M].北京:科学出版社, 2003: 103-132.
[15]张钹,张铃.模糊商空间理论[J].软件学报, 2003, 14(4): 770-776.
[16]徐峰,张铃.基于商空间的非均匀粒度聚类分析[J].计算机工程, 2005, 31(3): 26-28.
[17] Zadeh L A. Fuzzy sets and information granularity[J]. Advances in Fuzzy Systems Applicationsand Theory, 1979, 11(6): 3-18.
[18] Jerry R, Hobbs. Granularity[C]. In Proc. IJCAI, Los Angeles, 1985: 423-425.
[19] Lin T Y. Granular Computing on Binary Relations I: Data Mining and Neighborhood Systems[C].In: Rough Sets in Knowledge Discovery, Heidelberg(Germany): Physica-Verlag, 1998: 107-121.
[20] Lin T Y. Granular fuzzy sets : a view form rought set and probability theories[J]. InternationalJournal of Fuzzy Systems, 2001, 3(2): 373-381.
[21]张钹,张铃.问题求解理论及应用-商空间粒度计算理论及应用[M].北京:清华大学出版社,2007, 34-37.
[22] Pawlak Z. Granularity of knowledge, indiscernibiity and rough sets[C]. Proceedings of 1998 IEEEInternational Conference Fuzzy Systems, 1998: 106-110.
[23] Zadeh L A. Toward a Theroy of Fuzzy Information Granualation and Its Centrality in HumanReasoning and Fuzzy Logic[J]. Fuzzy Sets and Systems, 1997, 19(2): 111-127.
[24] Yao Y Y. Induction of Classication Rules by Granular Computing[C]. Proceedings of the ThirdInternational Conference on RSCTC’2002, Springer 2002: 331-338.
[25] Skowron A. Toward Intelligent Systems: Calculi of Information Granulars[C]. Proceedings ofInternational Workshop on Rough Set Theroy and Granular Computing(RSTGC’2001), Japan,2001: 300-307.
[26] Yao Y Y. Perspectives of granular computing[C]. 2005 IEEE Conference on Granular Computing,Beijing, 2005: 85-90.
[27]刘清,刘群.粒及粒计算在逻辑推理中的应用[J].计算机研究与发展, 2004, 41(4): 546-55l.
[28]李鸿.粒集理论:粒计算的一个新模型.重庆邮电大学学报[J]. 2007, 19 (8): 397-404.
[29]王国胤,张清华,胡军.粒计算研究综述[J].智能系统学报, 2007, 2(6): 8-26.
[30]程伟,石扬,张燕平.粒度计算的三种主要方法[J].计算机技术与发展, 2007, 17(3): 90-94.
[31]李道国,苗夺谦,张红云.粒度计算的理论、模型与方法[J].复旦学报, 2004, 43(5): 837-841.
[32]苗夺谦,王国胤,刘清等.粒计算：过去、现在与展望[M].北京:科学出版社, 2007:1-20.
[33] Yao Y Y. The rise of granular computing[J]. Joural of Chongqing University of Posts andTelecommunications(Natural Science Edition), 2008, 20{3}: 299-308.
[34] Juan Jiang, Hong Ding, Tao Peng. Research of the Modle about the Application of“GrC”in DataFusion System[C]. 2006 IEEE International Conference on”GrC”, Atlanta, 2006: 704-707.
[35]苗夺谦,范世栋.知识粒度的计算及其应用[J].系统工程理论与实践, 2002, 1(1): 48-56.
[36]李道国,苗夺谦,张红云.粒度计算的基本问题和可能解决的方案设计[J].计算机科学,2004, 31(2): 195-198.
[37]白硕,李国杰.聚类/分类中的粒度原理[J].计算机学报, 2002, 25(8): 810-816.
[38]张丽娟,李舟军,陈火旺.粒度计算及其数据挖掘中的应用[J].计算机科学, 2005, 32(12):178-180.
[39]刘仁金,黄贤武.图像分割的商空间粒度原理[J].计算机学报, 2005, 28(10): 1680-1685.
[40] Zhang L, Zhang B. A quotient space approximation model of multire solution signal analysis[J].Journal of Computer Science and Technology, 2005, 20(1): 90-94.
[41]何明,冯博琴.基于嫡和信息粒度的粗糙集聚类算法[J].西安交通大学学报, 2005, 39(4):343-346.
[42]毛军军,张铃,吴涛.不同粒度世界的描述法-商空间法[J].计算机学报, 2004, 27(3):328-333.
[43]曲开社,翟岩慧.偏序集、包含度与形式概念分析[J].计算机学报, 2006, 29(2): 219-226.
[44]仇国芳,陈劲.概念格的规则约简与属性特征[J].浙江大学学报, 2007, 34(2): 158-162.
[45]王虹,张文修.基于概念格的形势背景的知识约简[J].模式识别与人工智能, 2005, 18(6),641-645.
[46] Ivkovic I, Kontogiannis K. Towards automatic establishment of model dependencies using formalconcept analysis[J].International Journal of Software Engineering and Knowledge Engineering(IJSEKE), 2006, 16(4): 499-522.
[47] Qu K S, Liang J Y, Wang J H, et a1. The algebraic properties of concept lattice[J]. Journal ofSystems Science and Information, 2004, 2(2): 271-277.
[48] Tonella. Using a concept lattice of decomposition slices for program understanding and impactanalysism[J]. IEEE Transactions on Software Engineering, 2003, 29(6): 495-509.
[49] Xie ZhiPeng, Liu ZongTian. Concept lattice and association rule discovery[J]. Journal ofComputer Research&Development, 2000, 37(12): 1415-1421.
[50] Priss U, Old L J. Modeling lexical databases with formal concept analysis[J]. Universal ComputeSci, 2004, 10(8): 967-984.
[51]阎丽红.基于粒度计算的属性约简算法研究[J].计算机应用与软件, 2008, 25(4): 239-240.
[52]史进玲.基于粒计算的决策表属性约简与规则提取研究[D].河南:河南师范大学, 2009.
[53]蒋思宇,卢炎生.两种新的决策表属性的约简概念[J].小型微型计算机系统, 2006, 27(3):512-515.
[2]唐笑林.数据挖掘技术的研究和应用[J].华东理工大学学报(自然科学版), 2008, 34(2):290-295.
[3] Mallach G E. Decision Support and Data Warehouse Systems[M]. Beijing: Publishing House ofElectronics Industry, 2001: 414-415.
[4] Studer R, Benjamins V R, Fensel D. Knowledge engineering, principles and methods[J]. Data andKnowledge Engineering, 1998, 25(22): 161-197.
[5]李善平.本体论研究综述[J].计算机研究与发展, 2004, 41(7): 1041-1052.
[6]刘清,孙辉,王洪发.粒计算研究现状及基于粗糙集逻辑语义的粒计算研究[J].计算机学报,2008, 31(4): 543-555.
[7]华蓓,钟诚.数据挖掘中的隐私保护技术进展分析[J].微电子学与计算机, 2007, 26(8):38-41.
[8]王国胤,张清华,胡军.粒计算研究综述[J].智能系统学报, 2007, 2(6): 8-26.
[9]谢克明,逯新红,陈泽华.粒计算的基本问题和研究[J].计算机工程与应用, 2007, 43(16):41-44.
[10]李道国,苗夺谦,张红云.粒度计算的理论、模型与方法[J].复旦学报, 2004, 43(5): 837-841.
[11]王飞跃.词计算和语言动力学系统的计算理论框架[J].模式识别与人工智能, 2001, 14(4):371-384.
[12]陈欢.基于粗糙集理论的值约简及规则提取[J].福州大学学报, 2004, 32(4): 472-475.
[13]刘清,孙辉.从粗糙集的发展前景看粒计算的研究趋势[J].南昌工程学院学报, 2006, 5(25):1-10.
[14]张文修,吴志伟,梁吉业,等.粗糙集理论与方法[M].北京:科学出版社, 2003: 103-132.
[15]张钹,张铃.模糊商空间理论[J].软件学报, 2003, 14(4): 770-776.
[16]徐峰,张铃.基于商空间的非均匀粒度聚类分析[J].计算机工程, 2005, 31(3): 26-28.
[17] Zadeh L A. Fuzzy sets and information granularity[J]. Advances in Fuzzy Systems Applicationsand Theory, 1979, 11(6): 3-18.
[18] Jerry R, Hobbs. Granularity[C]. In Proc. IJCAI, Los Angeles, 1985: 423-425.
[19] Lin T Y. Granular Computing on Binary Relations I: Data Mining and Neighborhood Systems[C].In: Rough Sets in Knowledge Discovery, Heidelberg(Germany): Physica-Verlag, 1998: 107-121.
[20] Lin T Y. Granular fuzzy sets : a view form rought set and probability theories[J]. InternationalJournal of Fuzzy Systems, 2001, 3(2): 373-381.
[21]张钹,张铃.问题求解理论及应用-商空间粒度计算理论及应用[M].北京:清华大学出版社,2007, 34-37.
[22] Pawlak Z. Granularity of knowledge, indiscernibiity and rough sets[C]. Proceedings of 1998 IEEEInternational Conference Fuzzy Systems, 1998: 106-110.
[23] Zadeh L A. Toward a Theroy of Fuzzy Information Granualation and Its Centrality in HumanReasoning and Fuzzy Logic[J]. Fuzzy Sets and Systems, 1997, 19(2): 111-127.
[24] Yao Y Y. Induction of Classication Rules by Granular Computing[C]. Proceedings of the ThirdInternational Conference on RSCTC’2002, Springer 2002: 331-338.
[25] Skowron A. Toward Intelligent Systems: Calculi of Information Granulars[C]. Proceedings ofInternational Workshop on Rough Set Theroy and Granular Computing(RSTGC’2001), Japan,2001: 300-307.
[26] Yao Y Y. Perspectives of granular computing[C]. 2005 IEEE Conference on Granular Computing,Beijing, 2005: 85-90.
[27]刘清,刘群.粒及粒计算在逻辑推理中的应用[J].计算机研究与发展, 2004, 41(4): 546-55l.
[28]李鸿.粒集理论:粒计算的一个新模型.重庆邮电大学学报[J]. 2007, 19 (8): 397-404.
[29]王国胤,张清华,胡军.粒计算研究综述[J].智能系统学报, 2007, 2(6): 8-26.
[30]程伟,石扬,张燕平.粒度计算的三种主要方法[J].计算机技术与发展, 2007, 17(3): 90-94.
[31]李道国,苗夺谦,张红云.粒度计算的理论、模型与方法[J].复旦学报, 2004, 43(5): 837-841.
[32]苗夺谦,王国胤,刘清等.粒计算：过去、现在与展望[M].北京:科学出版社, 2007:1-20.
[33] Yao Y Y. The rise of granular computing[J]. Joural of Chongqing University of Posts andTelecommunications(Natural Science Edition), 2008, 20{3}: 299-308.
[34] Juan Jiang, Hong Ding, Tao Peng. Research of the Modle about the Application of“GrC”in DataFusion System[C]. 2006 IEEE International Conference on”GrC”, Atlanta, 2006: 704-707.
[35]苗夺谦,范世栋.知识粒度的计算及其应用[J].系统工程理论与实践, 2002, 1(1): 48-56.
[36]李道国,苗夺谦,张红云.粒度计算的基本问题和可能解决的方案设计[J].计算机科学,2004, 31(2): 195-198.
[37]白硕,李国杰.聚类/分类中的粒度原理[J].计算机学报, 2002, 25(8): 810-816.
[38]张丽娟,李舟军,陈火旺.粒度计算及其数据挖掘中的应用[J].计算机科学, 2005, 32(12):178-180.
[39]刘仁金,黄贤武.图像分割的商空间粒度原理[J].计算机学报, 2005, 28(10): 1680-1685.
[40] Zhang L, Zhang B. A quotient space approximation model of multire solution signal analysis[J].Journal of Computer Science and Technology, 2005, 20(1): 90-94.
[41]何明,冯博琴.基于嫡和信息粒度的粗糙集聚类算法[J].西安交通大学学报, 2005, 39(4):343-346.
[42]毛军军,张铃,吴涛.不同粒度世界的描述法-商空间法[J].计算机学报, 2004, 27(3):328-333.
[43]曲开社,翟岩慧.偏序集、包含度与形式概念分析[J].计算机学报, 2006, 29(2): 219-226.
[44]仇国芳,陈劲.概念格的规则约简与属性特征[J].浙江大学学报, 2007, 34(2): 158-162.
[45]王虹,张文修.基于概念格的形势背景的知识约简[J].模式识别与人工智能, 2005, 18(6),641-645.
[46] Ivkovic I, Kontogiannis K. Towards automatic establishment of model dependencies using formalconcept analysis[J].International Journal of Software Engineering and Knowledge Engineering(IJSEKE), 2006, 16(4): 499-522.
[47] Qu K S, Liang J Y, Wang J H, et a1. The algebraic properties of concept lattice[J]. Journal ofSystems Science and Information, 2004, 2(2): 271-277.
[48] Tonella. Using a concept lattice of decomposition slices for program understanding and impactanalysism[J]. IEEE Transactions on Software Engineering, 2003, 29(6): 495-509.
[49] Xie ZhiPeng, Liu ZongTian. Concept lattice and association rule discovery[J]. Journal ofComputer Research&Development, 2000, 37(12): 1415-1421.
[50] Priss U, Old L J. Modeling lexical databases with formal concept analysis[J]. Universal ComputeSci, 2004, 10(8): 967-984.
[51]阎丽红.基于粒度计算的属性约简算法研究[J].计算机应用与软件, 2008, 25(4): 239-240.
[52]史进玲.基于粒计算的决策表属性约简与规则提取研究[D].河南:河南师范大学, 2009.
[53]蒋思宇,卢炎生.两种新的决策表属性的约简概念[J].小型微型计算机系统, 2006, 27(3):512-515.