Hi,
I’m trying to run the Association Rule Learner (Borgelt) for different data sets (in a loop). For each dataset I’ve determined the minimum support which I control using a flow variable. I want to run the node each month. Every month the input data changes slightly. Some months I experience that the minimum support from last month is too low which causes the Association Rule Learner (Borgelt) to get stuck in “process running for X seconds”. Sometimes it gets up to 1000 seconds and when it finally does goes to the next phase of parsing this also takes a very long time, because there are obviously a lot of rules.
I was wondering if there is some way to run a process or a model or something where each month I can find the ideal minimum support automatically so that the process doesn’t run for too long but also doesn’t give an empty table (when minimum support is too high). I don’t want to keep adjusting the minimum support manually.
When I was thinking towards a solution I thought maybe there is a way to cancel a node when it’s running too long (for example more than 200 seconds) and then increase the minimum support with 1% or something and repeat this process until the node runs smoothly. But I haven’t found a way to actually implement something like this.
Can anyone help me with finding the ideal minimum support in an automated process?
Thanks!
Winanda Sheombarsing