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Regarding your first question, yes, you can use k-means. K-means can be used on data with any number of input features. Euclidean distances can be measured between points and cluster means in any dimensional space. With regards to which algorithm you should use for outlier detection, scikit-learn's website has a good walkthrough of some of the commonly used ...


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I think for kNN distance plays a bigger role. What happens to an (hyper) cube is analogous to what happens to the distance between points. As you increase the number of dimensions, the ratio between the closest distance to the average distance grows - this means that the nearest point is almost as far away as the average point, then it has only slightly more ...


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