粗糙集理论的研究及其在电力业务数据挖掘中的应用
摘要
RS理论是上世纪八十年代初由波兰数学家Pawlak提出的一种处理不精确知识的数学理论。其主要思想是利用已知的知识或信息来近似不精确的概念或现象。为快速高效地对海量GIS数据进行知识约简,分析了现有常见的约简算法及各自适用的范围。在此基础上,提出了新的基于遗传算法的约简方法,通过选取有效的适应度函数,很大程度上提高了对于大规模数据集的约简性能。并引入了自适应遗传算法的思想,使适应度函数中的因子能够随适应度的改变自动改变,从而增强了算法的全局寻优能力。通过对属性数据的实例分析,证明了该算法在海量数据约简方面的可行性和有效性。
VPRS理论则是对传统粗糙集理论的一种扩充,它通过设置阈值β,放松了对标准粗糙集理论近似边界的严格定义,增强了粗糙集模型的抗干扰能力,噪声阈值β的不同,将对约简结果产生不一样的影响,因此要在实际应用的时候合理选择。针对传统RS理论不能完全处理不完备信息系统的瓶颈问题,将经典RS理论进行了不同的扩充,促进了其在实用化方向的发展。RS与其他技术的相互融合是今后发展的一个方向,能够有效地解决RS应用过程中遇到的一些瓶颈问题。如近几年发展迅猛的云模型、粒计算以及概念格等,作者重点关注的是它们在解决GIS-SDM方面的成就。
通过在电网可视化管理系统(GVMS)平台中引入粗糙集理论的思想,可以在很大程度上增强系统的决策分析能力和电网的智能化管理。
Rough set theory which was proposed by Pawlak in the early 1980s is a new mathematical theory to deal with ambiguous and uncertain knowledge. The main idea of the theory is to export the question’s decision-making or classification rules through the knowledge reduction while preserving the consistency of classification. To reduce the massive data of GIS quickly and efficiently, the existing common attribute reduction algorithms and their respective scope of application are analyzed. And further more, a new reduction method based on genetic algorithm (GA) is presented and performance of this method is largely improved by choosing an effective fitness function. And it also introduces the idea of adaptive genetic algorithm to promise the factors of fitness function changed automatically with the value of fitness function, then it can increase the ability of global optimization. The feasibility and efficiency of the presented method is demonstrated by analyzing the examples of attribute data in power business.
Variable precision rough set is an extension of traditional rough set theory; it relaxes the restricted defining of approximation boundary in standard rough set theory and improves the anti-interference ability and Prediction ability to the new data of rough set model by setting up threshold valueβ. If noise thresholdβis different, the reduction results will be different too. Therefore the valueβshould be choice reasonably on the application. In view of the bottlenecks that traditional RS theory can not fully deal with incomplete information system, an expansion for traditional RS theory has been raised and contributed to the direction of RS practical development. A mutual confluence of RS and other technologies that concludes the cloud model, granular computing, and concept lattices, etc is a direction for future development that can solve a number of bottlenecks RS encountered effectively. The author will focus on their achievements in the settling the GIS-SDM.
The introducing of rough set theory in the Grid Visualization Management System (GVMS) platform could enhance the decision analysis capabilities and the intelligent management of network in a large part.
VPRS理论则是对传统粗糙集理论的一种扩充,它通过设置阈值β,放松了对标准粗糙集理论近似边界的严格定义,增强了粗糙集模型的抗干扰能力,噪声阈值β的不同,将对约简结果产生不一样的影响,因此要在实际应用的时候合理选择。针对传统RS理论不能完全处理不完备信息系统的瓶颈问题,将经典RS理论进行了不同的扩充,促进了其在实用化方向的发展。RS与其他技术的相互融合是今后发展的一个方向,能够有效地解决RS应用过程中遇到的一些瓶颈问题。如近几年发展迅猛的云模型、粒计算以及概念格等,作者重点关注的是它们在解决GIS-SDM方面的成就。
通过在电网可视化管理系统(GVMS)平台中引入粗糙集理论的思想,可以在很大程度上增强系统的决策分析能力和电网的智能化管理。
Rough set theory which was proposed by Pawlak in the early 1980s is a new mathematical theory to deal with ambiguous and uncertain knowledge. The main idea of the theory is to export the question’s decision-making or classification rules through the knowledge reduction while preserving the consistency of classification. To reduce the massive data of GIS quickly and efficiently, the existing common attribute reduction algorithms and their respective scope of application are analyzed. And further more, a new reduction method based on genetic algorithm (GA) is presented and performance of this method is largely improved by choosing an effective fitness function. And it also introduces the idea of adaptive genetic algorithm to promise the factors of fitness function changed automatically with the value of fitness function, then it can increase the ability of global optimization. The feasibility and efficiency of the presented method is demonstrated by analyzing the examples of attribute data in power business.
Variable precision rough set is an extension of traditional rough set theory; it relaxes the restricted defining of approximation boundary in standard rough set theory and improves the anti-interference ability and Prediction ability to the new data of rough set model by setting up threshold valueβ. If noise thresholdβis different, the reduction results will be different too. Therefore the valueβshould be choice reasonably on the application. In view of the bottlenecks that traditional RS theory can not fully deal with incomplete information system, an expansion for traditional RS theory has been raised and contributed to the direction of RS practical development. A mutual confluence of RS and other technologies that concludes the cloud model, granular computing, and concept lattices, etc is a direction for future development that can solve a number of bottlenecks RS encountered effectively. The author will focus on their achievements in the settling the GIS-SDM.
The introducing of rough set theory in the Grid Visualization Management System (GVMS) platform could enhance the decision analysis capabilities and the intelligent management of network in a large part.
引文
[1]施泉生等.电力企业决策支持系统原理及应用[M].北京:中国电力出版社,2007.
[2]王乐鹏,潘华等.电力企业信息化原理及应用[M].北京:中国电力出版社,2007.
[3]张文修,姚一豫等.粗糙集与概念格[M].西安:西安交通大学出版社,2006.
[4]张文宇,贾嵘.数据挖掘与粗糙集方法[M].西安:西安电子科技大学出版社,2007.
[5]陈文伟.数据仓库与数据挖掘教程[M].北京:清华大学出版社,2006.
[6]李德仁,王树良等.空间数据挖掘理论与应用[M].北京:科学出版社,2006.
[7] Pawlak, Z. Some Issues on Rough Sets [J]. Transactions on Rough Sets, 2004, 1:1-58.
[8]王彪等.粗糙集和模糊集的研究和应用[M].北京:电子工业出版社,2008.
[9] ZENG Xiao-hui, SHI Yi-bing, LIAN Yi. An Interpretation of Multi-pole Sonic Logging Data Mining Based on Rough Sets [J]. Journal of Communication and Computer, 2007, 4(1):8-10.
[10] SUN Tie-1i, JlAO Wei-wei. Applied Approaches of Rough Set Theory to Web Mining [J]. Journal of Donghua University, 2006, 23(6):117-120.
[11] Hu Jun, GUAN Chun, CHEN Yu-hai. Emotional agent based on rough set [J]. Journal of Communication and Computer, 2008, 5(5):16-20.
[12] JIA Ping, DAI Jian-hua, CHEN Wei-dong. Immune algorithm for discretization of decision systems in rough set theory [J]. Journal of Zhejiang University SCIENCE A,2006,7(4):602-606
[13] ZENG Chuan-hua, PEI Zheng, XU Yang. Knowledge Discovery for Event Series Decision Based on Rough Set [J]. Journal of Donghua University (Eng.Ed.), 2006, 23(6):93-96.
[14] GRZYM ALA-BUSSE Jerzy W. Mining incomplete data—a rough set approach [J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2008, 20(3):282-290.
[15] Qiang Shen, Richard Jensen.Rough Sets,Their Extensions and Applications[J]. International Journal of Automation and Computing, 2007, 04(3):217-228.
[16] ZIARKO Wojciech. Rough sets: the classical and extended views [J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2008, 20(3):254-265.
[17]王国胤等. Rough集理论与知识获取[M].西安:西安交通大学出版社,2001.
[18]梁雪峰,刘惠德,李俊付.基于粗集理论和云理论的GIS数据挖掘与知识发现[J].河北建筑科技学院学报,2004,21(2):82-89.
[19]易辉伟,曹红杰,王艳慧.基于空间数据仓库的GIS数据挖掘及其相关技术探讨[J].测绘工程,2002,11(3):45-61.
[20]张雪伍,苏奋振,石忆邵等.空间关联规则挖掘研究进展[J].地理科学进展,2007,26(6):119-127.
[21]王海起,王劲峰.空间数据挖掘技术研究进展[J].地理与地理信息科学,2005,21(4):6-10.
[22] RAINING R, WISE S, MAJ. Exploratory spatial data analysis in a geographic information system environment [J]. The Statistician,1998,47:457一469.
[23]陈文伟.数据仓库与数据挖掘教程[M].北京:清华大学出版社,2006.
[24]朱晓强,王行风.数据挖掘在GIS中的应用研究[J].计算机科学与工程,2003,28:208-213.
[25]乔梅,韩文秀.基于Rough集和数据库技术的属性约简算法[J].计算机工程,2005,31(6):102-105.
[26]代建华,李元香.粗集中属性约简的一种启发式遗传算法[J].西安交通大学学报,2002,36(12):1286-1290.
[27]何国建,陶宏才.一种基于粗集理论的属性约简改进算法[J].计算机应用,2004,24(11):75-80.
[28]邓雪清,董广军,范永弘.基于粗集理论的GIS属性数据挖掘[J].四川测绘,2003,26(4):147-150.
[29]康立军,吴丽丽.基于粗集理论的数据库知识发现的属性约简算法[J].甘肃农业大学学报,2005,5:689-692.
[30]康晓东.基于数据仓库的数据挖掘技术[M].北京:机械工业出版社,2003.
[31]马荣华,马晓冬,蒲英霞.从GIS数据库中挖掘空间关联规则研究[J].遥感学报,2005,9(6):734-741.
[32]陈彩云,李治国.关于属性约简和集合覆盖问题的探讨[J].计算机工程与应用,2004(2):44-46.
[33]楚扬杰,王先甲,方德斌等.基于粗糙集相关矩阵的属性约简算法[J].武汉理工大学学报,2006,28(2):81-83.
[34]李珊,肖怀铁,付强.改进的粗集属性约简的启发式算法[J].电光与控制,2006,13(4):46-48.
[35]苗夺谦,胡桂荣.知识约简的一种启发式算法[J].计算机研究与发展,1999,(5):393-400.
[36]梁雪峰等.基于粗集理论和云理论的GIS数据挖掘与知识发现[J].河北建筑科技学院学报,2004,21(2).
[37]李雯静等.粗集方法在GIS数据挖掘中的应用特点与方法研究[J].
[38]肖厚国,桑琳等.基于遗传算法的粗糙集属性约简及其应用[J].计算技工程与应用,2008,44(15):228-230.
[39]张亦军.基于粗糙集和遗传算法的大数据集数据挖掘应用研究[D].太原理工大学.2007.
[40]夏春艳.基于粗集属性约简的数据挖掘技术的研究与应用[D].长春理工大学.2007.
[41]孙秋野.基于粗糙集的数据挖掘技术在配电系统故障诊断中应用的研究[D].东北大学.2004.
[42]孙秋野,黎明.粗糙集理论及其电力行业应用[M].北京:机械工业出版社,2008.
[43]苗夺谦,王国胤等.粒计算:过去、现在与展望[M].北京:科学出版社,2007.
[44]何正友,张耀天.电网故障诊断方法研究综述[J].
[45]沈丽君,刘厚泉,杜振军.基于ARCGIS的配电网拓扑分析的实现[J].微计算机信息,2008(24):137-139.
[46]方刚.空间关联规则挖掘算法的研究与应用[D].电子科技大学.2009.
[47]孔祥明.基于变精度粗糙集的连续属性离散化方法及数据预处理方法[D].东北师范大学.2006.
[48]米据生,吴伟志,张文修等.基于变精度粗糙集理论的知识约简方法[J].系统工程理论与实践,2004,1(1).
[2]王乐鹏,潘华等.电力企业信息化原理及应用[M].北京:中国电力出版社,2007.
[3]张文修,姚一豫等.粗糙集与概念格[M].西安:西安交通大学出版社,2006.
[4]张文宇,贾嵘.数据挖掘与粗糙集方法[M].西安:西安电子科技大学出版社,2007.
[5]陈文伟.数据仓库与数据挖掘教程[M].北京:清华大学出版社,2006.
[6]李德仁,王树良等.空间数据挖掘理论与应用[M].北京:科学出版社,2006.
[7] Pawlak, Z. Some Issues on Rough Sets [J]. Transactions on Rough Sets, 2004, 1:1-58.
[8]王彪等.粗糙集和模糊集的研究和应用[M].北京:电子工业出版社,2008.
[9] ZENG Xiao-hui, SHI Yi-bing, LIAN Yi. An Interpretation of Multi-pole Sonic Logging Data Mining Based on Rough Sets [J]. Journal of Communication and Computer, 2007, 4(1):8-10.
[10] SUN Tie-1i, JlAO Wei-wei. Applied Approaches of Rough Set Theory to Web Mining [J]. Journal of Donghua University, 2006, 23(6):117-120.
[11] Hu Jun, GUAN Chun, CHEN Yu-hai. Emotional agent based on rough set [J]. Journal of Communication and Computer, 2008, 5(5):16-20.
[12] JIA Ping, DAI Jian-hua, CHEN Wei-dong. Immune algorithm for discretization of decision systems in rough set theory [J]. Journal of Zhejiang University SCIENCE A,2006,7(4):602-606
[13] ZENG Chuan-hua, PEI Zheng, XU Yang. Knowledge Discovery for Event Series Decision Based on Rough Set [J]. Journal of Donghua University (Eng.Ed.), 2006, 23(6):93-96.
[14] GRZYM ALA-BUSSE Jerzy W. Mining incomplete data—a rough set approach [J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2008, 20(3):282-290.
[15] Qiang Shen, Richard Jensen.Rough Sets,Their Extensions and Applications[J]. International Journal of Automation and Computing, 2007, 04(3):217-228.
[16] ZIARKO Wojciech. Rough sets: the classical and extended views [J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2008, 20(3):254-265.
[17]王国胤等. Rough集理论与知识获取[M].西安:西安交通大学出版社,2001.
[18]梁雪峰,刘惠德,李俊付.基于粗集理论和云理论的GIS数据挖掘与知识发现[J].河北建筑科技学院学报,2004,21(2):82-89.
[19]易辉伟,曹红杰,王艳慧.基于空间数据仓库的GIS数据挖掘及其相关技术探讨[J].测绘工程,2002,11(3):45-61.
[20]张雪伍,苏奋振,石忆邵等.空间关联规则挖掘研究进展[J].地理科学进展,2007,26(6):119-127.
[21]王海起,王劲峰.空间数据挖掘技术研究进展[J].地理与地理信息科学,2005,21(4):6-10.
[22] RAINING R, WISE S, MAJ. Exploratory spatial data analysis in a geographic information system environment [J]. The Statistician,1998,47:457一469.
[23]陈文伟.数据仓库与数据挖掘教程[M].北京:清华大学出版社,2006.
[24]朱晓强,王行风.数据挖掘在GIS中的应用研究[J].计算机科学与工程,2003,28:208-213.
[25]乔梅,韩文秀.基于Rough集和数据库技术的属性约简算法[J].计算机工程,2005,31(6):102-105.
[26]代建华,李元香.粗集中属性约简的一种启发式遗传算法[J].西安交通大学学报,2002,36(12):1286-1290.
[27]何国建,陶宏才.一种基于粗集理论的属性约简改进算法[J].计算机应用,2004,24(11):75-80.
[28]邓雪清,董广军,范永弘.基于粗集理论的GIS属性数据挖掘[J].四川测绘,2003,26(4):147-150.
[29]康立军,吴丽丽.基于粗集理论的数据库知识发现的属性约简算法[J].甘肃农业大学学报,2005,5:689-692.
[30]康晓东.基于数据仓库的数据挖掘技术[M].北京:机械工业出版社,2003.
[31]马荣华,马晓冬,蒲英霞.从GIS数据库中挖掘空间关联规则研究[J].遥感学报,2005,9(6):734-741.
[32]陈彩云,李治国.关于属性约简和集合覆盖问题的探讨[J].计算机工程与应用,2004(2):44-46.
[33]楚扬杰,王先甲,方德斌等.基于粗糙集相关矩阵的属性约简算法[J].武汉理工大学学报,2006,28(2):81-83.
[34]李珊,肖怀铁,付强.改进的粗集属性约简的启发式算法[J].电光与控制,2006,13(4):46-48.
[35]苗夺谦,胡桂荣.知识约简的一种启发式算法[J].计算机研究与发展,1999,(5):393-400.
[36]梁雪峰等.基于粗集理论和云理论的GIS数据挖掘与知识发现[J].河北建筑科技学院学报,2004,21(2).
[37]李雯静等.粗集方法在GIS数据挖掘中的应用特点与方法研究[J].
[38]肖厚国,桑琳等.基于遗传算法的粗糙集属性约简及其应用[J].计算技工程与应用,2008,44(15):228-230.
[39]张亦军.基于粗糙集和遗传算法的大数据集数据挖掘应用研究[D].太原理工大学.2007.
[40]夏春艳.基于粗集属性约简的数据挖掘技术的研究与应用[D].长春理工大学.2007.
[41]孙秋野.基于粗糙集的数据挖掘技术在配电系统故障诊断中应用的研究[D].东北大学.2004.
[42]孙秋野,黎明.粗糙集理论及其电力行业应用[M].北京:机械工业出版社,2008.
[43]苗夺谦,王国胤等.粒计算:过去、现在与展望[M].北京:科学出版社,2007.
[44]何正友,张耀天.电网故障诊断方法研究综述[J].
[45]沈丽君,刘厚泉,杜振军.基于ARCGIS的配电网拓扑分析的实现[J].微计算机信息,2008(24):137-139.
[46]方刚.空间关联规则挖掘算法的研究与应用[D].电子科技大学.2009.
[47]孔祥明.基于变精度粗糙集的连续属性离散化方法及数据预处理方法[D].东北师范大学.2006.
[48]米据生,吴伟志,张文修等.基于变精度粗糙集理论的知识约简方法[J].系统工程理论与实践,2004,1(1).