The Study of Fusing the Data Facing Environment Monitoring

Although the data fusion and data mining belong to the data processing technology, former researchers combine these technologies quite a few. In fact these technologies are nearly correlative, and both serve the goal of the knowledge detects. The target of data fusion is forming the data foundation of data mining, and the target of data mining is extracting the useful knowledge in the foundation of above data, to complete the knowledge detection. Therefore, our research's aim is combining the two technologies, designs the valid algorithm to carry on the data mining in the foundation of data fusion, mainly on the artificial intelligence theory and the Bayes method, excavates the valid information in the data of environmental monitoring as far as possible.

[1]  Yu Xin,et al.  Essential Methods and Progress of Information Fusion Theory , 2003 .

[2]  Abraham Kandel,et al.  Granular neural networks for numerical-linguistic data fusion and knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  Jie Tian,et al.  The research of test and evaluation for multisensor data fusion systems , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[4]  James Llinas,et al.  Revisiting the JDL Data Fusion Model II , 2004 .

[5]  Junbin Gao,et al.  Some remarks on Kalman filters for the multisensor fusion , 2002, Inf. Fusion.

[6]  Shu-Li Sun,et al.  Multi-sensor optimal information fusion Kalman filter , 2004, Autom..

[7]  Xiao De-yun.  Multi-sensor data fusion based on similitude degree , 2004 .

[8]  Yong Yan,et al.  A wavelet-based multisensor data fusion algorithm , 2004, IEEE Transactions on Instrumentation and Measurement.

[9]  Jean-Bernard Choquel,et al.  A new probabilistic and entropy fusion approach for management of information sources , 2004, Inf. Fusion.

[10]  L.Y. Pao,et al.  Tutorial on multisensor management and fusion algorithms for target tracking , 2004, Proceedings of the 2004 American Control Conference.

[11]  Cui Ping-yuan,et al.  Research on adaptive information fusion algorithm and its application , 2003 .

[12]  Tie-Jun Wu,et al.  Data mining in multisensor system based on rough set theory , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[13]  Gilbert Saporta,et al.  Data fusion and data grafting , 2002 .

[14]  E. L. Waltz.  Information understanding: integrating data fusion and data mining processes , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[15]  Marimuthu Palaniswami,et al.  Distributed data fusion using support vector machines , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[16]  Wendi Heinzelman,et al.  A general data fusion architecture , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.