Market basket analysis with networks

The field of market basket analysis, the search for meaningful associations in customer purchase data, is one of the oldest areas of data mining. The typical solution involves the mining and analysis of association rules, which take the form of statements such as “people who buy diapers are likely to buy beer.” It is well-known, however, that typical transaction datasets can support hundreds or thousands of obvious association rules for each interesting rule, and filtering through the rules is a non-trivial task [25]. One may use an interestingness measure to quantify the usefulness of various rules, but there is no single agreed-upon measure and different measures can result in very different rankings of association rules. In this work, we take a different approach to mining transaction data. By modeling the data as a product network, we discover expressive communities (clusters) in the data, which can then be targeted for further analysis. We demonstrate that our network ba...

• Type: Journal
• Year: 2011
• Where: SNAM
• Authors: Troy Raeder, Nitesh V. Chawla
• Keywords: Analysis, Association Rules, Interestingness Measure, SNAM 2011