Come up with a practical case for data mining, that could employ clustering with a new set of conditions that would allow group records and won’t fit into the existing paradigm of simple similarity with the equal treatment of all variables. For example, a dataset of anonymous commuting rides may be deanonymized with clustering analysis. Then the condition for clustering may be to find rides with a similar departure and arrival points, and that had to happen around the same time of a day, but no more than one ride in the same cluster may be conducted at the same day (one person can not ride two vehicles on the same route around the same time on the same day). Then the clusters of similar rides conducted on different days may suggest the same commuter. In your responses, discuss the fit of the proposed conditions to the proposed case for at least two posts by other students, and identify and discuss (if possible) any two or more cases that have similar conditions structure, so they can be solved with the same generalized algorithm.
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