The CURE algorithm is a method of clustering data. An outline of it can be found here on slide 5: https://www.slideshare.net/ellepiu/cure-clustering-algorithm. I personally learnt it from this video: https://www.youtube.com/watch?v=JrOJspZ1CUw, where the algorithm is explained at 7 minutes.
This is my understanding of the process of CURE. Let's assume that the data set is large enough such that we have to store a portion of the data on disk.
Take an amount of the data set that fits onto the computer, and cluster (perhaps hierarchically) to initialise clusters.
For each cluster: pick k representative points, and move each a fixed distance, $\alpha$, towards the cluster centroid.
For each data point on disk, assign it to the cluster with the representative point it is closest to.
To me, this selection of representative points seems like a really arbitrary thing to do, and I don't really see the logical process behind choosing to do this.
For any two data sets representing the same thing, it could be possible (even likely) that one set of clusters pre-representative-point-moving for one data set would be the same as the set of clusters post-representative-point-moving for the other.
Could someone please help explain in more detail the reasoning behind doing this? (Beyond just the basic pseudo-algorithm I have seen.)
I think that when moving the representative points towards the centroid of each cluster, this is equivalent to shrinking the boundary of the cluster. Once this is done, the data points that remain outside of the boundary are eliminated as clusters. But I'm not sure if this is correct.