Intelligent Systems in Modeling Phase of Information Mining Development Process

  • Sebastian Martins
  • Patricia Pesado
  • Ramón García-Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9799)


The Information Mining Engineering (IME) understands in processes, methodologies, tasks and techniques used to: organize, control and manage the task of finding knowledge patterns in information bases. A relevant task is selecting the data mining algorithms to use, which it is left to the expertise of the information mining engineer, developing it in a non-structured way. In this paper we propose an Information Mining Project Development Process Model (D-MoProPEI) which provides an integrated view in the selection of Information Mining Processes Based on Intelligent Systems (IMPbIS) within the Modeling Phase of the proposed Process Model through a Systematic Deriving Methodology.


Bayesian Network Class Attribute Brand Loyalty Data Mining Algorithm Field Description 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research reported in this paper was partially funded by Project ME-SPU-PROMINF-UNLa-2015-2017 of the Argentinean Ministry of Education and Project UNLa-33A205 of the Secretary of Science and Technology of National University of Lanus (Argentina).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sebastian Martins
    • 1
    • 3
  • Patricia Pesado
    • 2
  • Ramón García-Martínez
    • 3
  1. 1.PhD Program on Computer ScienceNational University of La PlataLa PlataArgentina
  2. 2.III-LIDI. Computer Sc SchoolNational University of La Plata – CIC Bs asLa PlataArgentina
  3. 3.Information Systems Research GroupNational University of LanusLanúsArgentina

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