Signal Clustering Using Temporal Logics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10548)


This paper introduces a new method for clustering signals using their temporal logic properties. Specifically, we propose a hierarchical clustering algorithm for efficiently processing a set of input signals. The input data is unlabeled, that is, no further information about properties of the signals are available to the learning algorithm other than the signals themselves. The algorithm produces a hierarchical structure where the internal nodes test some temporal properties of the data, and each terminal node contains a cluster (i.e., a group of similar signals). Each cluster can be mapped to a Signal Temporal Logic (STL) formula that describes its signals. The obtained formulae can be used directly for monitoring purposes but also, more generally, to acquire knowledge about the system under analysis. We present two case studies to illustrate the characteristics of our proposed algorithm. The first case study is related to a maritime surveillance problem, and the second is a fault classification problem in an automatic transmission system.


Signal Temporal Logic Specification mining Clustering Knowledge discovery Formal methods Unsupervised learning Logic inference 



This work was partially supported by DENSO CORPORATION and by the Office of Naval Research under grant N00014-14-1-0554. The authors would like to acknowledge Hirotoshi Yasuoka (DENSO CORPORATION) and Rachael Ivison (Boston University) for providing valuable feedback during this research. We also thank the anonymous reviewers for their comments.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Boston UniversityBostonUSA

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