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Declarative Modeling for Machine Learning and Data Mining

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7278)

Abstract

Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have 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 machine learning and data mining problems as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the machine learning or 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 machine learning and 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 using our results on constraint programming for itemset mining [1] and probabilistic programming. Some further ideas along these lines are contained in [2].

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References

  1. Guns, T., Nijssen, S., De Raedt, L.: Itemset mining: A constraint programming perspective. Artificial Intelligence 175(12-13), 1951–1983 (2011)

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  2. De Raedt, L., Nijssen, S.: Towards Programming Languages for Machine Learning and Data Mining (Extended Abstract). In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 25–32. Springer, Heidelberg (2011)

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© 2012 Springer-Verlag Berlin Heidelberg

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De Raedt, L. (2012). Declarative Modeling for Machine Learning and Data Mining. In: Domenach, F., Ignatov, D.I., Poelmans, J. (eds) Formal Concept Analysis. ICFCA 2012. Lecture Notes in Computer Science(), vol 7278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29892-9_2

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  • DOI: https://doi.org/10.1007/978-3-642-29892-9_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29891-2

  • Online ISBN: 978-3-642-29892-9

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