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I'm not sure how to phrase this, or even if such a thing exists. Sorry!

I have a bunch of data points, which are mostly pretty tight. Each group should hone in on a specific point in space. Unfortunately, some of the recordings are polluted with movements (i.e. a bump of the table). These movements always end where they started, so the data points that follow are similar to those that precede the movement.

These movements affect a very small proportion of the data points, so I've just been ignoring them so far and have just used the mean of the co-ordinates to determine the location. What I'd like is a function that weights each data point according to it's similarity to the others, such that a data point during a movement would have a smaller weighting than a static point.

Is there a name for this? How is it done?

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    You might find something like that if you do a search about clustering methods. Keep in mind that the task there is different.2011-06-09
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    It's possible that I have misinterpreted your question in my answer below. If that's the case, please add a sample of your data (and perhaps a graph) so that we can see what you mean.2011-06-09
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    From what I've heard, it's more customary to just throw away some outliers. I may be wrong, though.2011-06-09
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    Sounds like what you're looking for are [robust statistics](http://en.wikipedia.org/wiki/Robust_statistics), which are automatically resistant to outliers.2011-06-09

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