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)

Abstract

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.

References

  1. 1.
    Thomsen, E.: BI’s promised land. Intell. Enterp. 6(4), 21–25 (2003)Google Scholar
  2. 2.
    Negash, S., Gray, P.: Business intelligence. In: Bursteiny, F., Holsapple, C. (eds.) Handbook on Decision Support Systems 2. IHIS, pp. 175–193. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Langseth, J., Vivatrat, N.: Why proactive business intelligence is a hallmark of the real-time enterprise: outward bound. Intell. Enterp. 5(18), 34–41 (2003)Google Scholar
  4. 4.
    Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.: Business process intelligence. Comput. Ind. 53(3), 321–343 (2004)CrossRefGoogle Scholar
  5. 5.
    Michalski, R., Bratko, I., Kubat, M.: Machine Learning and Data Mining, Methods and Applications. Wiley, New York (1998)Google Scholar
  6. 6.
    Michalski, R.: A theory and methodology of inductive learning. Artif. Intell. 20, 111–161 (1983)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Quinlan, J.: Learning logic definitions from relations. Mach. Learn. 5, 239–266 (1990)Google Scholar
  8. 8.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)CrossRefMATHGoogle Scholar
  9. 9.
    Heckerman, D., Chickering, M., Geiger, D.: Learning bayesian networks, the combination of knowledge and statistical data. Mach. Learn. 20, 197–243 (1995)MATHGoogle Scholar
  10. 10.
    García-Martínez, R., Britos, P., Rodríguez, D.: Information Mining Processes Based on Intelligent Systems. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, Jan (eds.) IEA/AIE 2013. LNCS, vol. 7906, pp. 402–410. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    García-Martínez, R., Britos, P., Pesado, P., Bertone, R., Pollo-Cattaneo, F., Rodríguez, D., Pytel, P., Vanrell. J.: Towards an information mining engineering. In: Software Engineering, Methods, Modeling and Teaching, pp. 83–99. Medellín University Press. ISBN: 978-958-8692-32-6 (2011)Google Scholar
  12. 12.
    Martins, S., Pesado, P., García-Martínez, R. (2014). Process Mining Proposal for Information Mining Engineering: MoProPEI (in spanish). Latin-American Journal of Software Engineering, 2(5): 313–332. http://dx.doi.org/10.18294/relais.2014.313-332. ISSN: 2314-2642
  13. 13.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37–54 (1996)Google Scholar
  14. 14.
    Chapman, P., Clinton, J., Keber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 Step by step BI guide. Edited by SPSS (2000)Google Scholar
  15. 15.
    Marbán, Ó., Mariscal, G., Menasalvas, E., Segovia, J.: An engineering approach to data mining projects. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 578–588. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Britos, P., Jiménez Rey, E., García-Martínez, E.: Work in progress: programming misunderstandings discovering process based on intelligent data mining tools. In: Proceedings 38th ASEE/IEEE Frontiers in Education Conference (2008)Google Scholar
  17. 17.
    Kaufmann, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)CrossRefGoogle Scholar
  18. 18.
    Grabmeier, J., Rudolph, A.: Techniques of Cluster Algorithms in Data Mining. Data Min. Knowl. Disc. 6(4), 303–360 (2002)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ferrero, P., Britos, P., García-Martínez, R.: Detection of Breast Lesions in Medical Digital Imaging Using Neural Networks. In: Debenham, J. (ed.) IEA/AIE 2008. IFIP, vol. 218, pp. 1–10. Springer, Boston (2008)CrossRefGoogle Scholar
  20. 20.
    Britos, P., Cataldi, Z., Sierra, E., García-Martínez, R.: Pedagogical protocols selection automatic assistance. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds.) IEA/AIE 2008. LNCS (LNAI), vol. 5027, pp. 331–336. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Britos, P., Grosser, H., Rodríguez, D., García-Martínez, R.: Detecting unusual changes of users consumption. In: Bramer, M. (ed.) IEA/AIE 2008. IFIP, vol. 276, pp. 297–306. Springer, Boston (2008)CrossRefGoogle Scholar
  22. 22.
    Britos, P., Felgaer, P., García-Martínez, R.: Bayesian networks optimization based on induction learning techniques. In: Bramer, M. (ed.) IEA/AIE 2008. IFIP, vol. 276, pp. 439–443. Springer, Boston (2008)CrossRefGoogle Scholar
  23. 23.
    Britos, P., Abasolo, M., García-Martínez, R., Perales, F.: Identification of MPEG-4 patterns in human faces using data mining techniques. In: Proceedings 13th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2005, pp. 9–10 (2005)Google Scholar
  24. 24.
    Cogliati, M., Britos, P., García-Martínez, R.: Patterns in temporal series of meteorological variables using SOM & TDIDT. In: Bramer, M. (ed.) AITP. IFIP, vol. 217, pp. 305–314. Springer, Boston (2006)CrossRefGoogle Scholar
  25. 25.
    Martins, S., Rodríguez, D., García-Martínez, R.: Deriving processes of information mining based on semantic nets and frames. In: Ali, M., Pan, J.-S., Chen, S.-M., Horng, M.-F. (eds.) IEA/AIE 2014, Part II. LNCS, vol. 8482, pp. 150–159. Springer, Heidelberg (2014)CrossRefGoogle Scholar

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

Personalised recommendations