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Now, we study another interesting issue called mining negative correlations.
So we first need to distinguish rare patterns and negative patterns.
What is a rare pattern?
Rare pattern usually means there are some rare occurring items,
they have very low support but they are interesting.
We want to catch such patterns.
For example, buying Rolex watches.
How to mine such patterns?
We previously already discussed this.
For different item sets like for those rare items, we should be able to
set some individualized group based and minimum supports threshold.
That means for rare patterns for just those items,
we should set a rather low minimum support threshold,
then we'll be able to capture such patterns.
But negative patterns could be another very different one.
Negative patterns is those patterns that are negatively correlated.
That means they are unlikely happen together.
So for example, if you find some customer, the same customer,
who buys Ford Expedition, which is a SUV car, and
also a Ford Fusion, a hybrid car, together.
So they are unlikely to happen together, so
we called these patterns negative correlated patterns.
The problem becomes how to define such patterns?
We may have one support-based definition like this.
We say, if the itemsets A and B getting together their
support is far less than sup(A) x sup(B),
that means a chance to get together is far less than random, okay?
Then we can say A and B are negatively correlated.
Is this a good definition?
Actually, this definition may remind us the definition of lift.