characteristic relational patterns
Research in relational data mining has two major directions: finding global models of a relational database and the discovery of local relational patterns within a database. While relational patterns show how attribute values co-occur in detail, their huge numbers hamper their usage in data analysis. Global models, on the other hand, only provide a summary of how different tables and their attributes relate to each other, lacking detail of what is going on at the local level. In this paper we introduce a new approach that combines the positive properties of both directions: it provides a detailed description of the complete database using a small set of patterns. More in particular, we utilise a rich pattern language and show how a database can be encoded by such patterns. Then, based on the MDLprinciple, the novel RDB-KRIMP algorithm selects the set of patterns that allows for the most succinct encoding of the database. This set, the code table, is a compact description of the database in terms of local relational patterns. We show that this resulting set is very small, both in terms of database size and in number of its local relational patterns: a reduction of up to 4 orders of magnitude is attained.
relational data mining mdl frequent patterns