we can seefor examplethat there were transactions of which land were bought together of these also included so the estimated conditional probability is / compared to the fact that only of all transactions included this gives us lift of the need to have decent number of transactions in these counts in order to be able to make relatively solid inferences is why we must first select frequent itemsets if we were to generate rules from an infrequent itemsetthe counts would be very smalldue to thisthe relative values would be meaningless (or subject to very large error barsnote that there are many more association rules that have been discovered from this dataset datasets are required to support at least minimum baskets and minimum lift of this is still small dataset when compared to what is now possible with the webwhen you perform millions of transactionsyou can expect to generate many thousandseven millionsof rules howeverfor each customeronly few of them will be relevant at any given timeand so each costumer only receives small number of recommendations more advanced basket analysis there are now other algorithms for basket analysis that run faster than apriori the code we saw earlier was simpleand was good enough for us as we only had circa thousand transactions if you have had many millionsit might be worthwhile to use faster algorithm (although note that for most applicationslearning association rules can be run offlinethere are also methods to work with temporal information leading to rules that take into account the order in which you have made your purchases to take an extreme example of why this may be usefulconsider that someone buying supplies for large party may come back for trash bags therefore it may make sense to propose trash bags on the first visit howeverit would not make sense to propose party supplies to everyone who buys trash bag you can find python open source implementations ( new bsd license as scikit-learnof some of these in package called pymining this package was developed by barthelemy dagenais and is available at