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Which is the best method for clustering 1D data?
KDE is maybe the most sound method for clustering 1-dimensional data. With KDE, it again becomes obvious that 1-dimensional data is much more well behaved. In 1D, you have local minima; but in 2D you may have saddle points and such “maybe” splitting points.
What’s the rule of thumb for clustering in KDE?
How much they add is dependent on the choice of a smoothing kernel with radius h. A good choice for h is the Silverman’s rule of thumb: h =std ( x )* (4/3/n) ^ (1/5). For example, the density at point i, data x, number of elements n, and often-used Gaussian/normal kernel would be: For full KDE, that’s an unseemly n ^2 checks.
Which is better em or k-means for clustering?
EM is better than k-means in terms of results. K-means, however, has a faster run-time. They will produce similar results if the standard deviation/covariance matrices are approximately equal. If you suspect this is true, use k-means. DBSCAN is used when the data is non-gaussian.
Are there saddle points in a 1D cluster?
In 1D, you have local minima; but in 2D you may have saddle points and such “maybe” splitting points. See this Wikipedia illustration of a saddle point, as how such a point may or may not be appropriate for splitting clusters.
Which is more special single dimension or clustering?
A single dimension is much more special than you naively think, because you can actually sort it, which makes things a lot easier. In fact, it is usually not even called clustering, but e.g. segmentation or natural breaks optimization. You might want to look at Jenks Natural Breaks Optimization and similar statistical methods.
What is the EPs for clustering in DBSCAN?
The above example clusters points into a group, such that each element in a group is at most eps away from another element in the group. This is like the clustering algorithm DBSCAN with eps=0.2, min_samples=1.