Association Analysis / Rule Learning

"Association Analysis" or "Association Rule Learning" is the discovery of interesting relationships between variables in large datasets. The relationships are usually represented in the form of the association rules containing antecedent and consequent.

For e.g. {Onions, Oil} => {Tomatoes}. Here antecedent is Onions and Oil and consequent are Tomatoes. This implies that customers, who buy onions and oil both, are very likely to buy tomatoes as well. How much is it likely that a customer who buys onions and oil also buy tomatoes?  This measure is given by the confidence of the rule. Such rules are generated from databases having many transactions. We would ideally want many transactions where a customer bought the three items together (to be really sure). This measure is the support of the rule.

Two most popular algorithms for learning “association rules” are “Apriori” and “FP grow”.

Arithmetic mean

In our everyday life, we use a lot of statistics without even knowing it. Calculating an arithmetic mean is very common. For e.g. average age of students in a class, average income in a company.

Arithmetic mean can be calculated by the formula .

What this formula says is that we give equal weightage to each of our values. We can rewrite this formula in the following form to make this clear.