D5, if you see this is 1,110 solved cases.

So what you probably can see, is from coffee point of view,

like a coffee guy may say, mc are likely getting together,

because they get along as 100 cases buying coffee but not milk.

1000 cases we're buying both coffee and milk.

But for milk guy, they probably say, they are very unlikely getting together,

because I got 10,000 cases buying milk but not coffee.

But only 1000 case buying milk and coffee.

So, in that case you look at different measures.

It's interesting to see, All Confidence and

Jaccard they also say it's closer to zero, unlikely getting together.

But Max Confidence says it's close to one, they are very likely getting together.

Then we look at a Kulczynski said, I'm right in the middle,

because the tug of the war on each side is ten to one.

Then Cosine said, I'm a little prone to unlikely getting together.

Now, we change this one even more.

This is 1,000 to ten, or 1,000 to 100,000.

The coffee guys said they are very, very likely getting together.

But the milk guy said, they are very unlikely getting together.

Now in this case, you probably can see All Confidence and

Jaccard drop down to 0.01, and even cosine dropped down to 0.1.

But if Max Confidence says, I am very confident they are very close to one,

because they are very likely getting together.

But in Kulcyzynski said, I'm still in the neutral because this is 100 to one,

the other is one to 100, they have the equal ratio.

So, which one do you like?

So, probably we can see D4 to D6.

The real case is, that differentiate that five null-invariant measures.

But we probably can see, Kulcyzynski measure

holds firm when in these very imbalanced cases.

But the ratio is balanced on both sides, and it holds firm at 0.5.

That looks interesting.

But on the other hand, we also know those cases, some are very imbalanced,

we may want to introduce another measure called imbalance ratio.

The imbalanced ratio is introduced in the sense,

the support of item set A and support of item set B,

their differences play important role in this imbalance ratio computation.

Then you proceed for the same cases in the last three,

the Kulcyzynski vector holds firms at 0.5.

But the imbalance ratio, okay, D sub four cases is zero,

because they are already balanced.

And D sub five cases become 0.89, they are rather imbalanced.

And D sub six cases, it is very imbalanced.

So imbalance ratio, really can show you how balanced the two sides are.

So we feel Kulcyzynski plus imbalance ratio, these two things getting together

will present a clear picture for all the three data sets, D4 through D6.