Machine learning and Data Mining - Association Analysis with Python

A list of transactions from a grocery store is shown in the figure above. Frequent items are a list of items that commonly appear together. One example is {wine, diapers, soy milk}. From the data set we can also find an association rule such as diapers - wine. This means that if someone buys diapers, there is a good chance they will buy wine. With the frequent item sets and association rules retailers have a much better understanding of their customers. Although common examples of association rulea are from the retail industry, it can be applied to a number of other categories, such as web site traffic, medicine, etc. How do we define these so called relationships? Who defines what is interesting? When we are looking for frequent item sets or association rules, we must look two parameters that defines its relevance. The support of an itemset, which is defined as the percentage of the data set which containts this itemset.