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2013 Volume 28
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RESEARCH ARTICLE   Open Access    

Using automated planning for improving data mining processes

  • 1.

    Departamento de Informática

  • 2.

    S.A.U

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  • Abstract: This paper presents a distributed architecture for automating data mining (DM) processes using standard languages. DM is a difficult task that relies on an exploratory and analytic process of processing large quantities of data in order to discover meaningful patterns. The increasing heterogeneity and complexity of available data requires some expert knowledge on how to combine the multiple and alternative DM tasks to process the data. Here, we describe DM tasks in terms of Automated Planning, which allows us to automate the DM knowledge flow construction. The work is based on the use of standards that have been defined in both DM and automated-planning communities. Thus, we use PMML (Predictive Model Markup Language) to describe DM tasks. From the PMML, a problem description in PDDL (Planning Domain Definition Language) can be generated, so any current planning system can be used to generate a plan. This plan is, again, translated to a DM workflow description, Knowledge Flow for Machine Learning format (Knowledge Flow file for the WEKA (Waikato Environment for Knowledge Analysis) tool), so the plan or DM workflow can be executed in WEKA.
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  • Cite this article

    Susana Fernández, Tomás de la Rosa, Fernando Fernández, Rubén Suárez, Javier Ortiz, Daniel Borrajo, David Manzano. 2013. Using automated planning for improving data mining processes. The Knowledge Engineering Review 28(2)157−173, doi: 10.1017/S0269888912000409
    Susana Fernández, Tomás de la Rosa, Fernando Fernández, Rubén Suárez, Javier Ortiz, Daniel Borrajo, David Manzano. 2013. Using automated planning for improving data mining processes. The Knowledge Engineering Review 28(2)157−173, doi: 10.1017/S0269888912000409
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RESEARCH ARTICLE   Open Access    

Using automated planning for improving data mining processes

  • 1.

    Departamento de Informática

  • 2.

    S.A.U

  • Published online: 07 February 2013
The Knowledge Engineering Review  28,  2013, 28(2): 157−173  |  Cite this article

Abstract: Abstract: This paper presents a distributed architecture for automating data mining (DM) processes using standard languages. DM is a difficult task that relies on an exploratory and analytic process of processing large quantities of data in order to discover meaningful patterns. The increasing heterogeneity and complexity of available data requires some expert knowledge on how to combine the multiple and alternative DM tasks to process the data. Here, we describe DM tasks in terms of Automated Planning, which allows us to automate the DM knowledge flow construction. The work is based on the use of standards that have been defined in both DM and automated-planning communities. Thus, we use PMML (Predictive Model Markup Language) to describe DM tasks. From the PMML, a problem description in PDDL (Planning Domain Definition Language) can be generated, so any current planning system can be used to generate a plan. This plan is, again, translated to a DM workflow description, Knowledge Flow for Machine Learning format (Knowledge Flow file for the WEKA (Waikato Environment for Knowledge Analysis) tool), so the plan or DM workflow can be executed in WEKA.

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    Acknowledgments

    • This work has been partially supported by the Spanish MICINN under projects TIN2008-06701-C03-03, TRA-2009-008, the regional projects CCG08-UC3M/TIC-4141, and the Automated User Knowledge Building (AUKB) project funded by Ericsson Research Spain.

    • This has to do mainly with how the heuristic is computed.

    • http://archive.ics.uci.edu/ml/datasets.html

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    Susana Fernández, Tomás de la Rosa, Fernando Fernández, Rubén Suárez, Javier Ortiz, Daniel Borrajo, David Manzano. 2013. Using automated planning for improving data mining processes. The Knowledge Engineering Review 28(2)157−173, doi: 10.1017/S0269888912000409
    Susana Fernández, Tomás de la Rosa, Fernando Fernández, Rubén Suárez, Javier Ortiz, Daniel Borrajo, David Manzano. 2013. Using automated planning for improving data mining processes. The Knowledge Engineering Review 28(2)157−173, doi: 10.1017/S0269888912000409
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