1.
In CLIQUE, the threshold used to find cluster density remains constant,
even as the number of dimensions increases. This is a potential problem
since density drops as dimensionality increases; i.e., to find clusters in higher
dimensions the threshold has to be set at a level that may well result in the
merging of low-dimensional clusters. Comment on whether you feel this is
truly a problem and, if so, how you might modify CLIQUE to address this
problem.
Name at least one situation in which you would not want to use clustering based on SNN similarity or density.
Give an example of a set of clusters in which merging based on the closeness
of clusters leads to a more natural set of clusters than merging based on the
strength of connection (interconnectedness) of clusters.
We take a sample of adults and measure their heights. If we record the gender of each person, we can calculate the average height and the variance of the height, separately, for men and women. Suppose, however, that this information was not recorded. Would it be possible to still obtain this information? Explain.
(a) How might a set of term clusters defined by the top terms in a document cluster differ from the word clusters found by clustering the terms with K-means?
(b) How could term clustering be used to define clusters of documents?
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