Association rule mining

What is rule mining? Why do we use the association rule mining? So in a given transaction with multiple items, it tries to find the rules that govern how or why such items are often bought together. It is intended to identify strong rules discovered in databases using some measures of interestingness. Based on the concept of strong rules , Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale systems in supermarkets.

It identifies frequent if-then associations , which are called association rules. An association rule has two parts: an antecedent (if) and a consequent (then). It’s majorly used by retailers, grocery stores, an online marketplace that has a large transactional database.

It finds: features (dimensions) which occur together. It is often used by grocery stores, e-commerce websites, and anyone with large transactional databases. This rule shows how frequently a itemset occurs in a transaction.

A typical example is Market Based Analysis. It is an ideal method to use to discover hidden rules in the asset data. In it, frequent Mining shows which items appear together in a transaction or relation. Take an example of a Super Market where customers can buy variety of items.

Usually, there is a pattern in what the customers buy. For instance, mothers with babies buy baby products such as milk and diapers. Damsels may buy makeup items whereas bachelors may buy beers and chips etc. In particular, the rules describe specific relations between variables in the dataset.

We can define the value of a rule using a number of different measures. The one that we use in Weka, the most popular association rule algorithm, is called Apriori. I don’t know if you remember the weather data from Data Mining with Weka. Here’s this little dataset with instances and a few attributes.

Well, here are some association rules. Since most transactions data is large, the apriorialgorithm makes it easier to find these patterns or rulesquickly. Of course, the algorithm must be decided based on the use-case and the user’s mindset.

By finding correlations and. For example, understanding customer buying habits. This technique was introduced for the purpose of discovering regularities between products in large-scale transaction data in supermarkets. One of the earlier applications of association rule mining revealed that people buying beer often also bought diapers.

So this is one example of an association rule. Another association rule could be cheese and ham and bread implies butter. It has achieved great success in a plethora of applications such as market basket, computer networks, recommendation systems, and healthcare. The confidence value indicates how reliable this rule is.

The relationships between co-occurring items are expressed as association rules. A portion of the data set is shown below. Input Data Format, select Data in binary matrix format.

There are three common ways to measure association. Measure 1: Support. This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears.

Association rules are often used to analyze sales transactions.