Sensor signal clustering with Self-Organizing Maps
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
7-12-2015
Abstract
Contemporary sensor data are generally large data streams, possibly at a high sampling rate, making data analysis and visualization complex and computationally intensive. We present a novel clustering method for the evaluation of signal data. We are interested in clustering the signals based on the similarity of their behavior (shape), which contains more information than the signal intensity and the dominant frequencies. The signals are encoded into symbol strings. We use the edit distance to determine the similarity between strings. Based on this similarity, we cluster the data streams into a SOM-type network. This SOM is dynamic and adapts incrementally to the input sensor data stream. Incoming signals are processed on the fly and the system has the capability to “forget” old signals. Our method is particularly useful for the inspection of signal streams, both in the context of on-line monitoring and off-line analysis, and can be used as a component in a visualization dashboard.
Recommended Citation
Popovici, R., & Andonie, R. (2015). Sensor signal clustering with Self-Organizing Maps. 2015 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2015.7280576
Journal
2015 International Joint Conference on Neural Networks (IJCNN)
Rights
Copyright © 2015, IEEE
Comments
This article was originally published in 2015 International Joint Conference on Neural Networks (IJCNN). The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.