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The two are combined to help find "interesting" rules. As you know,

$$ \newcommand{\Kulczynski}{{\rm Kulczynski}} \newcommand{\support}{{\rm support}} \Kulczynski = \frac{1}{2}\big(P(A|B) + P(B|A)\big) $$

If Kulczynski is near 0 or 1, then we have an interesting rule that is negatively or positively associated respectively. If Kulczynski is near 0.5, then we may or may not have an interesting rule. We can have

$$ \Kulczynski = \frac{1}{2}\big(0.5 + 0.5\big) = 0.5 $$

Also, as in your case, we might also have

$$ \Kulczynski = \frac{1}{2}\big(0.863 + 0.012) = 0.4375 $$

While some people might consider these both uninteresting, others might want to know about this. To differentiate between the two situations, we can look at Imbalance Ratio where 0 is perfectly balanced and 1 is very skewed.

$$ IR = \frac{\big|\support(A) - \support(B)\big|}{\support(A) - \support(B) - \support(A \cup B)} $$

So completely uninteresting rules would have both $\Kulczynski=0.5$ and $IR=0$.

The two are combined to help find "interesting" rules. As you know,

$$ \newcommand{\Kulczynski}{{\rm Kulczynski}} \newcommand{\support}{{\rm support}} \Kulczynski = \frac{1}{2}\big(P(A|B) + P(B|A)\big) $$

If Kulczynski is near 0 or 1, then we have an interesting rule that is negatively or positively associated respectively. If Kulczynski is near 0.5, then we may or may not have an interesting rule. We can have

$$ \Kulczynski = \frac{1}{2}\big(0.5 + 0.5\big) = 0.5 $$

Also, as in your case, we might also have

$$ \Kulczynski = \frac{1}{2}\big(0.863 + 0.012) = 0.4375 $$

While some people might consider these both uninteresting, others might want to know about this. To differentiate between the two situations, we can look at Imbalance Ratio where 0 is perfectly balanced and 1 is very skewed.

$$ IR = \frac{\big|\support(A) - \support(B)\big|}{\support(A) + \support(B) - \support(A \cup B)} $$

So completely uninteresting rules would have both $\Kulczynski=0.5$ and $IR=0$.

The two are combined to help find "interesting" rules. As you know,

$$ Kulczynski = \frac{1}{2}\big(P(A|B) + P(B|A)\big) $$

If Kulczynski is near 0 or 1, then we have an interesting rule that is negatively or positively associated respectively. If Kulczynski is near 0.5, then we may or may not have an interesting rule. We can have

$$ Kulczynski = \frac{1}{2}\big(0.5 + 0.5\big) = 0.5 $$

Also, as in your case, we might also have

$$ Kulczynski = \frac{1}{2}\big(0.863 + 0.012) = 0.4375 $$

While some people might consider these both uninteresting, others might want to know about this. To differentiate between the two situations, we can look at Imbalance Ratio where 0 is perfectly balanced and 1 is very skewed.

$$ IR = \frac{\big|support(A) - support(B)\big|}{support(A) - support(B) - support(A \cup B)} $$

So completely uninteresting rules would have both $Kulczynski=0.5$ and $IR=0$.

The two are combined to help find "interesting" rules. As you know,

$$ \newcommand{\Kulczynski}{{\rm Kulczynski}} \newcommand{\support}{{\rm support}} \Kulczynski = \frac{1}{2}\big(P(A|B) + P(B|A)\big) $$

If Kulczynski is near 0 or 1, then we have an interesting rule that is negatively or positively associated respectively. If Kulczynski is near 0.5, then we may or may not have an interesting rule. We can have

$$ \Kulczynski = \frac{1}{2}\big(0.5 + 0.5\big) = 0.5 $$

Also, as in your case, we might also have

$$ \Kulczynski = \frac{1}{2}\big(0.863 + 0.012) = 0.4375 $$

While some people might consider these both uninteresting, others might want to know about this. To differentiate between the two situations, we can look at Imbalance Ratio where 0 is perfectly balanced and 1 is very skewed.

$$ IR = \frac{\big|\support(A) - \support(B)\big|}{\support(A) - \support(B) - \support(A \cup B)} $$

So completely uninteresting rules would have both $\Kulczynski=0.5$ and $IR=0$.

The two measures are discussed in "Re-examination of interestingness measures in pattern mining: a unified framework" by Tianyi Wu, Yuguo Chen and Jiawei Han and can be combined to help find "interesting" rules. As you know,

$$ Kulczynski = \frac{1}{2}\big(P(A|B) + P(B|A)\big) $$

If Kulczynski is near 0 or 1, then we have an interesting rule that is negatively or positively associated respectively. If Kulczynski is near 0.5, then we may or may not have an interesting rule. We can have

$$ Kulczynski = \frac{1}{2}\big(0.5 + 0.5\big) = 0.5 $$

Also, as in your case, we might also have

$$ Kulczynski = \frac{1}{2}\big(0.863 + 0.012) = 0.4375 $$

While some people might consider these both uninteresting, others might want to know about this. To differentiate between the two situations, we can look at Imbalance Ratio where 0 is perfectly balanced and 1 is very skewed.

$$ IR = \frac{\big|support(A) - support(B)\big|}{support(A) + support(B) - support(A \cup B)} $$

So completely uninteresting rules would have both $Kulczynski=0.5$ and $IR=0$.

The two are combined to help find "interesting" rules. As you know,

$$ Kulczynski = \frac{1}{2}\big(P(A|B) + P(B|A)\big) $$

If Kulczynski is near 0 or 1, then we have an interesting rule that is negatively or positively associated respectively. If Kulczynski is near 0.5, then we may or may not have an interesting rule. We can have

$$ Kulczynski = \frac{1}{2}\big(0.5 + 0.5\big) = 0.5 $$

Also, as in your case, we might also have

$$ Kulczynski = \frac{1}{2}\big(0.863 + 0.012) = 0.4375 $$

While some people might consider these both uninteresting, others might want to know about this. To differentiate between the two situations, we can look at Imbalance Ratio where 0 is perfectly balanced and 1 is very skewed.

$$ IR = \frac{\big|support(A) - support(B)\big|}{support(A) - support(B) - support(A \cup B)} $$

So completely uninteresting rules would have both $Kulczynski=0.5$ and $IR=0$.