Topology looks for the patterns inside big data
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You could do a barcode for the holes of any fixed dimension, but as you point out, the 0-th dimension case is relatively uninteresting, and as you get to higher dimensions, it's harder to visualize and interpret what is going on. So dimension 1 is most common.
> the 0-th dimension case is relatively uninteresting
It was explained to me that the zeroth order Betti numbers have applications for clustering.
> It was explained to me that the zeroth order Betti numbers have applications for clustering.
That is correct, so perhaps "uninteresting" was too strong :) The 0-th Betti number counts the number of connected components of the space. So if we are at radius, say, 1 and the 0-th Betti number is 3, then we know the data points can be put into 3 "dense" clusters. By dense, I mean that for every two data points A and B in the cluster, there is a sequence of data points that you could step on going from A to B where each step has distance at most 1. I don't know if that explanation made any sense.
It's actually availible free from archive.org[0], which is a huge plus.
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