Mining Structured Association Patterns from Databases

We consider the data-mining problem of discovering structured association patterns from large databases. A structured association pattern is a set of sets of items that can represent a two level structure in some specified set of target data. Although the structure is very simple, it cannot be extracted by conventional pattern discovery algorithms. We present an algorithm that discovers all frequent structured association patterns. We were motivated to consider the problem by a specific text mining application, but our method is applicable to a broad range of data mining applications. Experiment with syntactic and real data show that our algorithm efficiently discovers structured association pattenrs in a large volume of data.