Applying association rules in medical diagnosis can be used to assist physicians while curing patients. The general problem of the induction of reliable diagnostic rules is hard as, theoretically, no induction process can guarantee the correctness of induced hypotheses by itself. Practically, diagnosis is not an easy process as it involves unreliable diagnosis tests and the presence of noise in training examples.

Nevertheless, association rules can be used to identify likely symptoms appearing together. A transaction, in this case, corresponds to a medical case, while symptoms correspond to items. When a patient is treated, a list of symptoms is recorded as one transaction.

A lot of research has gone into understanding the composition and nature of proteins; yet many things remain to be understood satisfactorily. It is now generally believed that amino-acid sequences of proteins are not random.

With association rules, it is possible to identify associations between different amino acids that are present in a protein. A protein is a sequences made up of 20 types of amino acids. Each protein has a unique three-dimensional structure, which depends on the amino-acid sequence; slight change in the sequence may change the functioning of protein. To apply association rules, a protein corresponds to a transaction, while amino acids and their structure corespond to the items.

Such association rules are desirable for enhancing our understanding of protein composition and hold the potential to give clues regarding the global interactions amongst some particular sets of amino acids occurring in the proteins. Knowledge of these association rules or constraints is highly desirable for synthesis of artificial proteins.

Censuses make a huge variety of general statistical information about the society available to both researchers and general public. The information related to population and economic census can be forecasted in planning public services (education, health, transport, and funds) as well as in business (for setting up new factories, shopping malls, or banks and even marketing particular products).

To discover frequent patterns, each statistical area (for example, municipality, city, and neighborhood) corresponds to a transaction, and the collected indicators correspond to the items.

The customer relationship management (CRM), as we briefly discussed in the previous chapters, is a rich source of data through which companies hope to identify the preference of different customer groups, products, and services in order to enhance the cohesion between their products and services and their customers.

Association rules can reinforce the knowledge management process and allow the marketing personnel to know their customers well in order to provide better quality services. For example, association rules can be applied to detect a change of customer behavior at different time snapshots from customer profiles and sales data. The basic idea is to discover changes from two datasets and generate rules from each dataset to carry out rule matching.

Based on records of a large number of transactions, association rule learning is well-suited to be applied to the data that is routinely collected in day-to-day IT operations, enabling IT Operations Analytics tools to detect frequent patterns and identify critical changes. IT specialists need to see the big picture and understand, for example, how a problem on a database could impact an application server.

For a specific day, IT operations may take in a variety of alerts, presenting them in a transactional database. Using an association rule learning algorithm, IT Operations Analytics tools can correlate and detect the frequent patterns of alerts appearing together. This can lead to a better understanding about how a component impacts another.

With identified alert patterns, it is possible to apply predictive analytics. For example, a particular database server hosts a web application and suddenly an alert about a database is triggered. By looking into frequent patterns identified by an association rule learning algorithm, this means that the IT staff needs to take action before the web application is impacted.

Association rule learning can also discover alert events originating from the same IT event. For example, every time a new user is added, six changes in the Windows operating system are detected. Next, in the Application Portfolio Management (APM), IT may face multiple alerts, showing that the transactional time in a database as high. If all these issues originate from the same source (such as getting hundreds of alerts about changes that are all due to a Windows update), this frequent pattern mining can help to quickly cut through a number of alerts, allowing the IT operators to focus on truly critical changes.