Data mining involves the use of sophisticated data analysis
tools to discover previously unknown, valid patterns and relationships in large
data sets. These tools can include statistical models, mathematical algorithms,
and machine learning methods (algorithms that improve their performance
automatically through experience, such as neural networks, Association Rules or
decision trees). Consequently, data mining consists of more than collecting and
managing data, it also includes analysis and prediction.
Association Rules is an approach that analyzes the
characteristics between sets of data in order to find out those similar
patterns or relationships that have been highly relied on in merchandising. By
analyzing the patterns of the customers’ purchase behaviors, association rules
are able to reveal the preferences of products, correlations between each
purchase and further predict the consumers’ choices. For example, when one
product is often purchased with another one, there is an association. This
technique is frequently used by retail stores to assist in areas such as
marketing, advertising, product promotion, and product placement.