Towards Automatic Generation of Conceptual Interpretation of Clustering

  • Alejandra Pérez-Bonilla
  • Karina Gibert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


In this paper the Methodology of conceptual characterization by embedded conditioning CCEC, oriented to the automatic generation of conceptual descriptions of classifications that can support later decision-making is presented, as well as its application to the interpretation of previously identified classes characterizing the different situations on a WasteWater Treatment Plant (WWTP). The particularity of the method is that it provides an interpretation of a partition previously obtained on an ill-structured domain, starting from a hierarchical clustering. The methodology uses some statistical tools (as the boxplot multiple, introduced by Tukey, which in our context behave as a powerful tool for numeric variables) together with some machine learning methods, to learn the structure of the data; this allows extracting useful information (using the concept of characterizing variable) for the automatic generation of a set of useful rules for later identification of classes. In this paper the usefulness of CCEC for building domain theories as models supporting later decision-making is addressed.


Hierarchical clustering class interpretation Knowledge Discovery and Data Mining 


  1. 1.
    Gordon, A.D.: Identifying genuine clusters in a classification. Computational Statistics and Data Analysis 18, 561–581 (1994)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Gibert, K., Pérez-Bonilla, A.: Ventajas de la estructura jerárquica del clustering en la interpretración automática de clasificaciones. In: III TAMIDA, pp. 67–76 (2005)Google Scholar
  3. 3.
    Gibert, K.: The use of symbolic information in automation of statistical treatment for ill-structured domains. AI Communications 9, 36–37 (1996)Google Scholar
  4. 4.
    Gibert, K., Roda, I.: Identifying characteristic situations in wastewater treatment plants. In: Workshop BESAI (ECAI), vol. 1, pp. 1–9 (2000)Google Scholar
  5. 5.
    Gibert, K., Aluja, T., Cortés, U.: Knowledge Discovery with Clustering Based on Rules. Interpreting results. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 83–92. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Metcalf, E.: Wastewater engineering treatament. Disposal and reuse. McGraw-Hill 4th edn. revised by George Tchobanoglous, Franklin L. Burton NY.US (2003)Google Scholar
  7. 7.
    Gibert, K., Nonell, et al.: Knowledge discovery with clustering: impact of metrics and reporting phase by using klass. Neural Network World 4/05, 319–326 (2005)Google Scholar
  8. 8.
    Tukey, J.: Exploratory Data Analysis. Addison-Wesley, Reading (1977)zbMATHGoogle Scholar
  9. 9.
    Gibert, K.: Técnicas híbridas de Inteligencia Artificial y Estadística para el descubrimiento de conocimiento y la minería de datos. In: Thompson (ed.) Tendencias de la minería de datos en España, pp. 119–130 (2004)Google Scholar
  10. 10.
    Gibert, K., Pérez-Bonilla, A.: Revised boxplot based discretization as a tool for automatic interpretation of classes from hierarchical cluster. In: Series Studies in Classification, D.A., pp. 229–237. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Fayyad, U., et al.: From Data Mining to Knowledge Discovery: An overview. In: Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press (1996)Google Scholar
  12. 12.
    Gibert, K., Pérez-Bonilla, A.: Taking advantage of the hierarchical structure of a clustering for automatic generation of classification interpretations. In: 4th EUSFLAT, España, pp. 524–529 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alejandra Pérez-Bonilla
    • 1
    • 2
  • Karina Gibert
    • 1
  1. 1.Dep. of Statistics and Operations Research, Technical University of Catalonia., Campus Nord; Edif. C5. C/ Jordi Girona 1-3; 08034 BarcelonaSpain
  2. 2.Department of Industrial Engineering, University of Santiago of Chile, Avda. Ecuador 3769, SantiagoChile

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