数据挖掘与数据融合相结合的信息处理技术研究
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摘要
随着全球信息化的快速发展,数据挖掘与数据融合这两种作为处理海量数据、提取有用信息的高新技术倍受瞩目。这两种技术处理数据的原理各不相同,但在功能上相互补充,整合这两种技术能更有效地解决工程中的实际问题。数据挖掘的首要问题是寻找数据来源,将来自不同样本的数据利用数据融合技术有效地加以综合,再进行数据挖掘。数据融合需要在已知的模型上进行,而数据挖掘技术则可自动地建立模型。将这两种技术进行深层次的结合与渗透,可协同完成复杂数据处理工作。
本文对数据挖掘与数据融合这两种技术进行了理论研究与应用探讨,研究内容及成果如下:
(1)针对数据融合系统难以获取先验信息,以及存在大量冗余数据的问题,提出了用粗糙集理论建立数据融合模型的方法。采用属性约简及规则约简的方法处理数据,剔除冗余信息,获取最简规则。根据粗糙集最简规则建立融合系统。通过粗糙集离散化算法将融合方法推广至连续域。仿真结果证明,该算法是切实可行的。
(2)将模糊理论与神经网络技术相结合应用于数据融合。研究了基于模糊神经网络的融合系统原理,建立了基于模糊神经网络的融合系统,给出了神经网络学习算法。仿真结果验证了算法的可行性。
(3)针对数据挖掘与数据融合技术在功能上互补的特点,研究了数据挖掘与数据融合的集成技术,完善了知识获取过程,提高了融合精度。并建立了一种新的集成系统,研究给出了该集成系统的原理以及工作过程。
(4)针对粗糙集与模糊神经网络各自的优缺点,提出了一种粗糙集——模糊神经网络模型。研究了利用粗糙集技术获取模糊规则的方法,通过模糊神经网络对融合规则进行推广,克服了单独使用一种方法建立融合系统时存在的缺陷。
(5)设计了一种基于粗糙集——模糊神经网络技术的数据挖掘与数据融合集成系统。研究给出了设计原理,提出了建立集成系统的具体步骤,以及该集成系统的算法流程图。研究了集成系统各部分的算法,构建了网络的拓扑结构,推导出了网络的学习公式。提出了一种基于粗糙集方法的初始隶属函数获取算法,仿真结果证明了该集成系统的可行性与有效性。
With the rapid development of information globalization, data mining and data fusion receive great attention as two promising technologies to deal with huge volume of data. Although the principles of both are different, they can complement each other. Integrating both technologies can efficiently solve practical engineering problem. The chief problem of data mining is to search data. The fusion system could efficiently synthesize the data from different samples. Data fusion is applied on virtual model, while data mining technology can automatically establish the model. Therefore, Once highly coupled they will be helpful to accomplish complicated data analysis automatically with effect.The dissertation researches the theories of data mining and data fusion. The hybrid system's basic model is presented. The main work and achievements are as follows:(1) In order to deal with redundancy data, a new data fusion model based on rough sets is presented. Applying reduction method, the fusion algorithm is extracted. This fusion model could deal with continuous data , by using the discretization algorithm . Simulation results its feasibility.(2) One kind of fuzzy neural network, which is used to fuse multi-source data, is proposed by combing the advantages of neural network and fuzzy inference. Learning algorithm is also presented. Simulation results its feasibility.(3) Data mining and data fusion have complementary functions. According to their characteristics, the theory of integrated data mining and data fusion system is proposed.(4) A new fuzzy neural network based on rough sets is presented. Rules are extracted from database using rough sets to initialize the structure of fuzzy neural network. The fuzzy neural network is trained with original data. By combining those advantages of the two theories, the result is more satisfactory.(5) One kind of integrated data mining and data fusion system's model is designed with fuzzy neural network based on rough sets. A principle diagram and flow chart of the integrated system are presented. The model of the network is constructed. Algorithm of this model is also studied. The simulation results prove that the proposed model shows
good performance in the learning speed and accuracy.
本文对数据挖掘与数据融合这两种技术进行了理论研究与应用探讨,研究内容及成果如下:
(1)针对数据融合系统难以获取先验信息,以及存在大量冗余数据的问题,提出了用粗糙集理论建立数据融合模型的方法。采用属性约简及规则约简的方法处理数据,剔除冗余信息,获取最简规则。根据粗糙集最简规则建立融合系统。通过粗糙集离散化算法将融合方法推广至连续域。仿真结果证明,该算法是切实可行的。
(2)将模糊理论与神经网络技术相结合应用于数据融合。研究了基于模糊神经网络的融合系统原理,建立了基于模糊神经网络的融合系统,给出了神经网络学习算法。仿真结果验证了算法的可行性。
(3)针对数据挖掘与数据融合技术在功能上互补的特点,研究了数据挖掘与数据融合的集成技术,完善了知识获取过程,提高了融合精度。并建立了一种新的集成系统,研究给出了该集成系统的原理以及工作过程。
(4)针对粗糙集与模糊神经网络各自的优缺点,提出了一种粗糙集——模糊神经网络模型。研究了利用粗糙集技术获取模糊规则的方法,通过模糊神经网络对融合规则进行推广,克服了单独使用一种方法建立融合系统时存在的缺陷。
(5)设计了一种基于粗糙集——模糊神经网络技术的数据挖掘与数据融合集成系统。研究给出了设计原理,提出了建立集成系统的具体步骤,以及该集成系统的算法流程图。研究了集成系统各部分的算法,构建了网络的拓扑结构,推导出了网络的学习公式。提出了一种基于粗糙集方法的初始隶属函数获取算法,仿真结果证明了该集成系统的可行性与有效性。
With the rapid development of information globalization, data mining and data fusion receive great attention as two promising technologies to deal with huge volume of data. Although the principles of both are different, they can complement each other. Integrating both technologies can efficiently solve practical engineering problem. The chief problem of data mining is to search data. The fusion system could efficiently synthesize the data from different samples. Data fusion is applied on virtual model, while data mining technology can automatically establish the model. Therefore, Once highly coupled they will be helpful to accomplish complicated data analysis automatically with effect.The dissertation researches the theories of data mining and data fusion. The hybrid system's basic model is presented. The main work and achievements are as follows:(1) In order to deal with redundancy data, a new data fusion model based on rough sets is presented. Applying reduction method, the fusion algorithm is extracted. This fusion model could deal with continuous data , by using the discretization algorithm . Simulation results its feasibility.(2) One kind of fuzzy neural network, which is used to fuse multi-source data, is proposed by combing the advantages of neural network and fuzzy inference. Learning algorithm is also presented. Simulation results its feasibility.(3) Data mining and data fusion have complementary functions. According to their characteristics, the theory of integrated data mining and data fusion system is proposed.(4) A new fuzzy neural network based on rough sets is presented. Rules are extracted from database using rough sets to initialize the structure of fuzzy neural network. The fuzzy neural network is trained with original data. By combining those advantages of the two theories, the result is more satisfactory.(5) One kind of integrated data mining and data fusion system's model is designed with fuzzy neural network based on rough sets. A principle diagram and flow chart of the integrated system are presented. The model of the network is constructed. Algorithm of this model is also studied. The simulation results prove that the proposed model shows
good performance in the learning speed and accuracy.
引文
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[2] 徐涛,杨国庆,陈松灿.数据融合的概念方法和应用.南京航空航天大学学报.1995年4月,第27卷,第2期:258～265
[3] Hall D L, Linas J.An introduction to multisensor data fusion[J].Proc IEEE, 1997, 85(1): 1～23
[4] 何友,王国宏等.《多传感器信息融合及应用》.北京:电子工业出版社,2000
[5] ABIDIMA, GONZALEZRC. Data fusion in robotics and machine intelligence[M].AcademicPress, INC. 1992
[6] 高翔,王勇.数据融合技术综述.计算机测量与控制.2002.10(11):706～709
[7] VARSHNEYPK. Distributed detection and data fusion [M]. NewYork: Springer-Verlag, 1996.
[8] FAYYADM, piatets-shapire G. Advances in knowledge discovery and data mining [M]. MenloparkCA:AAAl/MITPress.
[9] 蓝金辉,周兆英,李勇.混合结构的数据融合方法在目标识别中的应用[J].仪器仪表学报,2001,22(3):313～314.
[10] 关键,何友,彭应宁.多传感器分布式检测综述[J].系统工程与电子技术,2002,(12):11～15.
[11] VISWANATHANR, THOMOPOULOSSCA, TUMULURIR. Optimal serial distributed decision[J].IEEE Trans. onAES, 1981,17(4):501～510.
[12] 刘源,崔宁周,谢维信等.具有模糊信息和自学习权重的分布式检测算法[J].电子学报,1999,27(3):9～12.
[13] WaltzE. L., 1998. Information Understanding: Integrating Date Fusion and Data Mining Processes. ISCAS'98, Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, 6: 553～556.
[14] Piatets-ShapiroG, Frawley W., William J. (eds), 1991. Knowledge Discovery in Database.Menlo Park CA, AAAI Press/MIT Press.
[15] Fayyad U. M., Piatetsky-Shapiro G.. et al. (eds), 1996. Advances in Knowledge Discovery and Data Mining. Cambridge, MA, MIT Press.
[16] 何友,彭应宁.多传感器数据融合模型综述.清华大学学报(自然科学版).1996年,第36卷,第9期:14～20
[17] 王建海,方振和,李英.多传感器数据融合浅析.上海大学学报(自然科学版).1997年8月,第3卷,第4期:454～461
[18] Alhakeem S, et al A Unified Approach to the Design of Decentralized Detection System IEEE Trans AES, 1995, 31(1): 9～20
[19] TueciR, TsaiMJ. Comparison of ROCs for Various Sensor Fusion Schemes. SPIE(The Society of Photo-Optical Instrumentation Engineers). Sensor FusionⅢ, 1990,1306:81～89
[20] JlinasJ, et al. Data Fusion Technology Forest for C~3MIS. IEEE Conf Third International Conference on Command, Contral, Communication and Management Information System, 1989:148～158
[21] Baek Wonjang, et al. Optimalm-ary Data Fusion with Distributed Sensors. IEEE Trams AES, 1995,31(3): 1150～1152
[22] 杨华,林卉.数据融合的研究综述.矿山量测.2005年9月第3期:24～28
[23] 员巧云,程刚.近年来我国数据挖掘研究综述.情报学报.2005年4月,第24卷第2期:250～256
[24] Pawlak Z. Rough set. International Journal of Information and Computer Science, 1982
[25] Hart J, Kamber M. Data Mining: Concepts and Techniques. Morgan Kaufman Publishers, 2000
[26] Chen Ming syan, Ham Jiawei, Philip S Yu. Data Mining: An overview from a database perspective [J]. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6): 866～881.
[27] Faloutsos, C, Lin, K. FastMap: A fast algorithm for indexing, data mining and visualization of traditional and multimedia datasets. In Proc of 24th ACM SIGMOD, 1995
[28] Yairi T, Kato Y, Hofi K. Fault Detection by Mining Association Rules from House-keeping Data. Proc. Robotics and Automation in Space, 2001
[29] 王光宏,蒋平.数据挖掘综述.同济大学学报.2004年2月,第32卷第2期:246～252
[30] 何剑斌,郑启伦,彭宏.数据融合与数据挖掘的集成研究.计算机工程与应用.2002.18:204～206
[31] frawley W J, Piatetsky G. Knowledge discovery in database: An overview, Knowledge discovery in database[M].AAAI/MIr press 1991
[32] 刘同明,曾富贵,刘伟.数据融合与数据挖掘相集成的自动目标识别系统.计算机工程与应用.2002,3:92～95
[33] 王国胤.《Rough集理论与知识获取》.西安:西安交通大学出版社.2001.
[34] 曾黄麟.《智能计算》.重庆:重庆大学出版社.2004
[35] 魏海坤.《神经网络的结构设计与方法》.北京:国防工业出版社.2005
[36] 闻新,周露等.《Marlab神经网络仿真与应用》.北京:科学出版社.2003
[37] 张云涛,龚玲.《数据挖掘原理与技术》北京:电子工业出版社.2004
[38] 李永敏,朱善君,陈湘晖,张岱崎,韩曾晋.基于粗糙集理论的数据挖掘模型.清华大学学报(自然科学版),1999年,第39卷第1期:29～33.
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