Weekly Post topics:
Answer anyone one main question…
1. Many partitional clustering algorithms that automatically determine the number of clusters claim that this is an advantage. List two situations in which this is not the case.
Consider the mean of a cluster of objects from a binary transaction data set. What are the minimum and maximum values of the components of the mean? What is the interpretation of components of the cluster mean? Which components most accurately characterize the objects in the cluster?
3) You are given two sets of 100 points that fall within the unit square. One set of points is arranged so that the points are uniformly spaced. The other set of points is generated from a uniform distribution over the unit square.
Is there a difference between the two sets of points?
If so, which set of points will typically have a smaller SSE for K=10 clusters? The random set of points will have a lower SSE.
What will be the behavior of DBSCAN on the uniform data set? The random data set?
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