›› 2018, Vol. 33 ›› Issue (4): 807-822.doi: 10.1007/s11390-018-1857-9

Special Issue: Data Management and Data Mining

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification

Xin Xu1, Jiaheng Lu2, Wei Wang3, Member, CCF, ACM   

  1. 1 Laboratory of Science and Technology on Information System Engineering, Nanjing Research Institute of Electronics Engineering, Nanjing 210007, China;
    2 Department of Computer Science, University of Helsinki, Helsinki 00014, Finland;
    3 State Key Laboratory for Novel Software and Technology, Nanjing University, Nanjing 210046, China
  • Received:2017-03-03 Revised:2018-05-15 Online:2018-07-05 Published:2018-07-05
  • About author:Xin Xu received her Ph.D. degree in computer science from School of Computing, National University of Singapore, Singapore, in 2006. She is currently a senior research engineer in Science and Technology on Information System Engineering Laboratory in Nanjing Research Institute of Electronic Engineering, Nanjing. Her research interests are in the area of artificial intelligence, data mining, and pattern recognition.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61771177 and 61701454, the Natural Science Foundation of Jiangsu Province of China under Grant Nos. BK20160147 and BK20160148, and the Academy Project of Finland under Grant No. 310321.

It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy.

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