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.

Question

What will be an ideal response?

Answer 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.This is a real problem. A similar problem exists in association analysis. In
particular, the support of association patterns with a large number of items

is often low. To find such patterns using an algorithm such as Apriori is diffi-
cult because the low support threshold required results in a large number of

association patterns with few items that are of little interest. In other words,
association patterns with many items (patterns in higher-dimensional space)
are interesting at support levels (densities) that do not make for interesting
patterns when the size of the association pattern (number of dimensions) is
low. One approach is to decrease the support threshold (density threshold)
as the number of items (number of dimensions) is increased.