(Inductive) Quering environment for predictive clustering trees

Abstract

Inductive databases tightly integrate databases with data mining. Besides data, an inductive database also stores models that have been obtained by running data mining algorithms on the data. By means of a querying environment, the user can query the database and retrieve particular models. In this paper, we propose such a querying environment. It can be used for building new models and for searching through the database of previously built models. The models that we consider are so-called predictive clustering trees (PCTs). PCTs generalize decision trees and can be used for several prediction and clustering tasks, among others, (multi-objective) classification and regression. Our querying environment supports queries that contain size, error, and syntactic constraints on the PCTs and helps the user to quickly obtain the models of interest.