You are given a data set with 100 records and are asked to cluster the data. You use K-means to cluster the data, but for all values of K, 1 ? K ? 100, the K-means algorithm returns only one non-empty cluster. You then apply an incremental version of K-means, but obtain exactly the same result. How is this possible? How would single link or DBSCAN handle such data?
What will be an ideal response?
(a) The data consists completely of duplicates of one object.
(b) Single link (and many of the other agglomerative hierarchical schemes)
would produce a hierarchical clustering, but which points appear in
which cluster would depend on the ordering of the points and the exact
algorithm. However, if the dendrogram were plotted showing the prox-
imity at which each object is merged, then it would be obvious that the
data consisted of duplicates. DBSCAN would find that all points were
core points connected to one another and produce a single cluster.
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