Association analysis enables you to identify items that have an affinity for each other. It is frequently used to analyze transactional data (market basket data) to identify items that often appear together in transactions. It is useful for discovering interesting relationships hidden in large data sets. For example, grocery stores and online merchants use association analysis to strategically organize and recommend products that tend to be purchased together.
Association analysis is also used for identifying dependent or associated events. For example, you can identify car parts that seem to fail around the same time. In this application, car inspections are treated as the market baskets and you analyze the associations among groups of faulty parts found in each inspection.
Besides market basket data, association analysis is also applicable to other application domains such as bioinformatics, medical diagnosis, Web mining, and scientific data analysis.
There are two main issues that need to be addressed whenapplying association analysis to market basket data. First, discovering patterns from a large transaction data set can be computationally expensive. Second, some of the discovered patterns could be random, i.e. they may have happened simply by chance.