Sensor Signal Clustering with Self-Organizing Maps

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. Index Terms-Signal clustering, String clustering, Edit distance, Evolving Self-Organizing Maps.