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Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 807–822 | Cite as

Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification

  • Xin Xu
  • Jiaheng Lu
  • Wei Wang
Regular Paper
  • 45 Downloads

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.

Keywords

symbolic data analysis stochastic pattern fuzzy interval hierarchical clustering emitter identification 

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Science and Technology on Information System Engineering, Nanjing Research Institute of Electronics EngineeringNanjingChina
  2. 2.Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland
  3. 3.State Key Laboratory for Novel Software and TechnologyNanjing UniversityNanjingChina

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