(Inductive) Quering environment for predictive clustering trees
Journal
Proceedings of the Second Balkan Conference in Informatics
Date Issued
2005
Author(s)
Kocev, Dragi
Dzeroski, Sasho
Struyf, Jan
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.
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.
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