|
|
Uncertain Information Clustering Based on DempsterShafer Theory and HCM |
CAO KeJin1, ZHAO ZongGui2, JIANG Han1 |
1.Institute of Command and Automation, PLA University of Science and Technology, Nanjing 210007 2.Insititute of the 28th Research Institute, China Electronics Technology Group Corporation, Nanjing 210014 |
|
|
Abstract When analyzing multisource information, it is necessary to cluster the information according to their sources. In this paper, a problem of clustering multisource information denoted by evidence is investigated, and an evidence clustering standard is given. In addition, an idea of transformation from the evidence interspaces to Euclidean interspaces is presented in this paper, then the HCM is used to cluster the multisource information. Based on the theory, a simple example of passive sensors ESM tracking aerial target is demonstrated.
|
Received: 12 May 2005
|
|
|
|
|
[1] Sidenbladh H, Svenson P, Schubert J. Comparing Multi-Target Trackers on Different Force Unit Levels. Proc of the SPIE, 2004, 5429: 306-314 [2] Shafer G. A Mathematical Theory of Evidence. Princeton, USA: Princeton University Press, 1976 [3] Bergsten U, Schubert J, Svensson P. Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis. In: Proc of the 3rd International Conference on Knowledge Discovery and Data Mining. Newport Beach, USA, 1997, 127-130 [4] Klein L A. Sensor and Data Fusion Concepts and Applications. 2nd Edition. Beillingham, USA: SPIE Optical Engineering Press, 1999 [5] Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, USA: Kluwer Academic Publishers, 1981 [6] Schubert J. Fast Dempster-Shafer Clustering Using a Neural Network Structure. In: Proc of the 7th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Paris, France, 1998, 1438-1445 [7] Schubert J. A Neural Network and Iterative Optimization Hybrid for Dempster-Shafer Clustering. In: Bedworth M, Brien J O, eds. Proc of the International Conference on Data Fusion. Great Malvern, UK, 1998, 29-36 [8] Schubert J. Simultaneous Dempster-Shafer Clustering and Gradual Determination of Number of Clusters Using a Neural Network Structure. In: Proc of the Conference on Information, Decision and Control. Adelaide, Australia, 1999, 401-406 [9] Bengtsson M, Schubert J. Dempster-Shafer Clustering Using Potts Spin Mean Field Theory. Soft Computing, 2001, 5(3): 215-228 [10] Schubert J. On Nonspecific Evidence. International Journal of Intelligent Systems, 1993, 8(6): 711-725 [11] Smets P. The Transferable Belief Model for Quantified Belief Representation. In: Gabbay D M, Smets P, eds. Handbook of Defeasible Reasoning and Uncertainty Management Systems. Doordrecht, The Netherlands: Kluwer Academic Publishers, 1998, Ⅰ: 267-301 [12] Smets P. Belief Functions and the Transferable Belief Model. 2000. http://www.sipta.org/documentation/belief.pdf [13] Ross T J. Fuzzy Logic for Engineering Applications. New York, USA: The McGraw-Hill Companies, 1995 (Ross T J,著;钱同惠,沈其聪,译.模糊逻辑及其工程应用. 北京:电子工业出版社, 2001) |
|
|
|