Discovery of Relational Association Rules

Abstract

Within KDD, the discovery of frequent patterns has been studied in a variety of settings. In its simplest form, known from association rule mining, the task is to discover all frequent item sets, i.e., all combinations of items that are found in a sufficient number of examples. We present algorithms for relational association rule discovery that are well-suited for exploratory data mining. They offer the flexibility required to experiment with examples more complex than feature vectors and patterns more complex than item sets.