We present our perspective of database mining as the con uence of machine learning techniques and the performance emphasis of database technology. We describe three classes of database mining problems involving classi cation, associations, and sequences, and argue that these problems can be uniformly viewed as requiring discovery of rules embedded in massive data. We describe a model and some basic operations for the process of rule discovery. We show how the database mining problems we consider map to this model and how they can be solved by using the basic operations we propose. We give an example of an algorithm for classi cation obtained by combining the basic rule discovery operations. This algorithm not only is e cient in discovering classi cation rules but also has accuracy comparable to ID3, one of the current best classi ers.
The association rule mining problem can be described as follows REF .
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Computer Science