What are the best clustering algorithms to use in order to cluster data with more than 100 dimensions (sometimes even 1000). I would appreciate if you know any implementation in C, C++ or especially C#.
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What are the best clustering algorithms to use in order to cluster data with more than 100 dimensions (sometimes even 1000). I would appreciate if you know any implementation in C, C++ or especially C#.
It depends heavily on your data. See curse of dimensionality for common problems. Recent research (Houle et al.) showed that you can’t really go by the numbers. There may be thousands of dimensions and the data clusters well, and of course there is even one-dimensional data that just doesn’t cluster. It’s mostly a matter of signal-to-noise.
This is why for example clustering of TF-IDF vectors works rather well, in particular with cosine distance.
But the key point is that you first need to understand the nature of your data. You then can pick appropriate distance functions, weights, parameters and … algorithms.
In particular, you also need to know what constitutes a cluster for you. There are many definitions, in particular for high-dimensional data. They may be in subspaces, they may or may not be arbitrarily rotated, they may overlap or not (k-means for example, doesn’t allow overlaps or subspaces).