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)

Abstract

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.

Keywords

Hierarchical clustering class interpretation Knowledge Discovery and Data Mining 

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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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