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I am trying to analyse multivariate time series data sets. I have 6 signals for each event, representing 3 linear accelerations and 3 rotational velocities for a 40ms window. I am trying to find a way to cluster together similar events based of these 6 signals.

The method I am currently looking at is using FFT on each signal to reduce it to frequency bins, Then doing some sort of clustering algorithm on highest 3 amplitude frequencies or something along those lines.

My question is what sort of clustering algorithm should I be looking at to cluster my problem. If for example my problem has 100 events, 6 sensors, 3 frequency and amplitudes per sensor per event.

I am new to this type of signal processing so this methodology might not be feasible but I welcome any suggestions on a clustering algorithm or a completely other approach that you might think is better for my problem.

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I'd start off with something simple like vector quantization (VQ). This allows you to set up a group of code words (centroids of your clusters), and simply find the code word closest to any of your data samples.

There are more complex approaches, but VQ gives you a good, simple baseline. If it doesn't give you the performance you need, then you can move to more complex approaches. The next step, after simple VQ, would be to try $K$-means clustering. These notes give a good outline.

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