? 一种面向复杂符号数据的分层聚类方法及其在辐射源识别上的应用
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :807-822    DOI: 10.1007/s11390-018-1857-9
Special Issue on Software Engineering for High-Confidence Systems << Previous Articles | Next Articles >>
一种面向复杂符号数据的分层聚类方法及其在辐射源识别上的应用
Xin Xu1, Jiaheng Lu2, Wei Wang3, Member, CCF, ACM
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
Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification
Xin Xu1, Jiaheng Lu2, Wei Wang3, Member, CCF, ACM
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

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摘要 众所周知,符号数据变量可以多种形式出现,如数值区间、若干随机测量值组成的集合或若干离散值组成的集合。目前,大多数符号数据分析仍局限于区间型数据分析。尽管在随机测量值集合与混合符号数据分析方面有一些预先开展的工作,然而由于缺乏灵活和高效的混合符号数据处理机制,难以充分利用所有的符号数据变量。因此,本文创新性地提出一种采用加权Jaccard距离和全维度剪枝策略的复杂符号数据分层聚类方法,并结合辐射源识别应用验证了方法的可行性。大量实验表明我们的方法在计算效率和识别准确率方面优于其他符号数据方法。
关键词符号数据分析   随机模式   模糊区间   分层聚类   辐射源识别     
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.
Keywordssymbolic data analysis   stochastic pattern   fuzzy interval   hierarchical clustering   emitter identification     
Received 2017-03-03;
本文基金:

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.

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.
引用本文:   
Xin Xu, Jiaheng Lu, Wei Wang.一种面向复杂符号数据的分层聚类方法及其在辐射源识别上的应用[J]  Journal of Computer Science and Technology , 2018,V33(4): 807-822
Xin Xu, Jiaheng Lu, Wei Wang.Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification[J]  Journal of Computer Science and Technology, 2018,V33(4): 807-822
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