![]() We use the example groceries transactions data in the arules package. And the nice thing is: you can stay in your familiar R Studio environment! Spark MLlib and sparklyr Example Data set Or do both of the above points by using FPGrowth in Spark MLlib on a cluster. See this blog for some details on Apriori vs. use another algorithm, for example FP Growth, which is more scalable.use more computing power (or cluster of computing nodes).But when you have very huge data sets, you need to do something else, you can: For data sets that are not too big, calculating rules with arules in R (on a laptop) is not a problem. In R there is a package arules to calculate association rules, it makes use of the so-called Apriori algorithm. Other use cases for MBA could be web click data, log files, and even questionnaires. With association rules mining we can identify items that are frequently bought together. For each customer we know what the individual products (items) are that he has bought. The classical example is data in a supermarket. Market Basket Analysis or association rules mining can be a very useful technique to gain insights in transactional data sets, and it can be useful for product recommendation.
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