Hello all,

I recently began to work with knime and so far i am very excited about the possibilities. I got a transaction record set of a customer and i am trying to get the most out of it. So far i did a RFM analysis which was quite an easy task with the functions of the GroupBy node i was even able to calculate all variables in one node.

I also used the Item Set Finder (Borgelt) to analyse the basket. To do so i reduced the data set on two variables, the transaction id and the EAN code. then i filtered out the null values and did a GroupBy on the transaction ids and connected this to the Item Set Finder (Borgelt) and used the Apriori Algorithm. Everything worked very well but i didn´t find enough information so i can be sure i got everything right. So i hope somebody will be so kind to answer my question :)

1. There are many different algorithms and i am not sure which one to choose. Are there big differences in the resupts and if yes which one will be the best choice to use it in discovery mode (eg. if you don´t know very much about the used data yet).

2. The Description for the Minimum Support parameter is "The minimum support" but what is the minimum Support and how do i find out the ideal value?

3. Looking at the result i see two variables of interest: ItemSetSupport and RelativeItemSetSupport. I suppose ItemSetSupport is the number how often the Items in the variable ItemSet have been found together. Is RelativeItemSetSupport% the percentage of how often the two items have been bought together based on the oerall count of both item sold?

Any help would be greatly appreciated.

Best Regards,

Alex