◤Demonstration – KL-Collaps◢

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We will show how to set parameters of KL-Collaps. Assume that KL-Result returned a hypothesis shown in Figure 1.

Figure 1: KL-Miner output.

In this particular pattern, we have **row attribute** *BMI* and
**column attribute** *Systolic blood pressure*. *BMI* has values
*Normal*, *Overweight* and *Obese*. *Systolic
blood pressure* has continuous range discretized into seven intervals.

After invoking KL-Collaps, we see the initial screen and set parameters as in Figure 2.

Figure 2: KL-Collaps parameters.

Both analyzed attributes (*BMI* and *Systolic blood pressure*) are
ordinal, therefore we set the coefficients to be **intervals**. Further
coefficient types available are **subsets**, **cuts**, **left cuts**
and **right cuts**, they have the same meaning as in
4ft-Miner. The three numeric parameters have the following meaning.

**P**– a positive integer, it is the maximal number of strongest interactions that we want at the output,**Q**– a rational number from 0 to 1, if*C*is the Chi-square value of the strongest returned interaction, then KL-Collaps won't return an interaction with the Chi-square value below*C***Q**,**M**– non-negative rational number, it is a lower bound for the Chi-square statistic.

After running KL-Collaps, we see the output window as in Figure 3.

Figure 3: KL-Collaps output.

We see that the strongest interaction conforming to the given parameters is
the one between categories *BMI=Obese* and *Systolic
pressure = 150 to 170*. We can copy the output to the Windows'
clipboard. Numbers *a*, *r = a + b*,
*k = a+ c* are computed from the corresponding
2 * 2 table (see below).

By double clicking the highlighted pattern, we view the corresponding 2 * 2 table and further details, as in Figure 4.

Figure 4: KL-Collaps output detail.

The additional statistics that we see are

**Yule's q**–*(ad − bc)/(ad + bc)*,**Odds ratio**–*ad/bc*,**E-Confidence**–*(a + d)/(a + d + b + c)*.

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