We will be doing a couple of things here. First, we will analyze a small toy dataset belonging to a supermarket, by using a product contingency matrix of product pair purchases based on their frequency. Then we will move on to contingency matrices based on other metrics such as support, lift, and so on by using another dataset.
The data for our first matrix consists of the six most popular products sold at the supermarket and also the number of times each product was sold by itself and in combination with the other products. We have the data in the form of a data table captured in a csv file, as you can see in the following figure:
To analyze this data, we first need to understand what it depicts. Basically, each cell value denotes the number of times that product combination was sold. Thus, the cell combination (1, A) denotes the product combination (milk, milk), which is basically the number of times milk was bought. Another example is the cell combination...
