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Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification

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Abstract

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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Authors and Affiliations

  1. Laboratory of Science and Technology on Information System Engineering, Nanjing Research Institute of Electronics Engineering, Nanjing, 210007, China

    Xin Xu

  2. Department of Computer Science, University of Helsinki, Helsinki, 00014, Finland

    Jiaheng Lu

  3. State Key Laboratory for Novel Software and Technology, Nanjing University, Nanjing, 210046, China

    Wei Wang

Authors
  1. Xin Xu
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  2. Jiaheng Lu
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  3. Wei Wang
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Correspondence to Xin Xu.

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Xu, X., Lu, J. & Wang, W. Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification. J. Comput. Sci. Technol. 33, 807–822 (2018). https://doi.org/10.1007/s11390-018-1857-9

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  • DOI: https://doi.org/10.1007/s11390-018-1857-9

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