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Title: Declarative modeling for machine learning and data mining
Authors: De Raedt, Luc
Issue Date: Oct-2014
Host Document: Gestion de Données – Principes, Technologies et Applications (BDA 2014)
Conference: BDA edition:30 location:Autrans, France date:14-17 October 2014
Abstract: Today, it remains a challenge to develop applications and software that incorporates data mining. One reason is that the field has focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques.
I propose to alleviate these problems by applying the constraint programming methodology to machine learning and data mining and to specify data mining tasks as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the data mining problem is rather than having to outline how that solution needs to be computed. This corresponds to a model + solver- based approach to data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algorithm that computes a particular solution to a specific problem.
I shall illustrate this perspective by presenting our work on developing models as well as modeling languages for several data mining tasks. I shall include our recent results on the MiningZinc language and system, an extension of the MiniZinc framework for constraint programming.
Description: Keynote presentation
Publication status: published
KU Leuven publication type: AMa
Appears in Collections:Informatics Section

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