Conceptual clustering in structured databases: a practical approach

This paper describes a clustering system able to discover useful groupings in structured databases based in the COBWEB algorithm, to which it adds the ability to cluster structured objects.

Abstract

Many machine-learning (either supervised or unsu-pervised) techniques assume that data present them-selves in an attribute-value form. But this formal-ism is largely insufficient to account for many appli-cations. Therefore, much of the ongoing research now focuses on first-order learning systems. But complex formalisms lead to high computational complexities. On the other hand, most of the currently installed databases have been designed according to a formal-ism known as entity-relationship, and usually imple-mented on a relational database management system. This formalism is far less complex than first-order logic, but much more expressive than attribute-value lists. In that context, the database schema defines an abstraction space, and learning must occur at each level of abstraction. This paper describes a clustering system able to discover useful groupings in structured databases. It is based in the COBWEB algorithm, to which it adds the ability to cluster structured objects.