Presenter/Author Information

Karina Gibert
Alejandra Perez-Bonilla

Keywords

knowledge discovery and data mining, hierarchical clustering, class interpretation, induction rules, wastewater treatment plant

Start Date

1-7-2010 12:00 AM

Description

More and more, the analysis of clustering results becomes difficult as the number of variables considered increases, and the number of classes is not low. Sometimes concept induction methods are used to associate concepts to every class and use to be expressed as boolean expressions, easy to understand and supposedly providing good support to decision making. It has been seen that most of the concept induction algorithms priorize the compacity of the final expressions, as well as their predictive power. However, for descriptive purposes, when the meaning of classes has to be recognized and understood by the expert, this is not the best approach, since compacity directly implies elimination of redundancies or strong associations, while comprehension of the class is mainly based in understanding how variables interact among them inside the class. Here a method to induce conceptual descriptions of classes is proposed, providing non-minimal descriptions of the classes, but richer ones including the characteristics that distinguishes a class from the others, in such a way that expert can easily recognize the essence of the class, and conceptualize it on the bases of local interactions among all variables observed inside every class. This kind of interpretations provide an excellent support for later decision support systems.

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Jul 1st, 12:00 AM

Automatic interpretation of classes for improving decision support

More and more, the analysis of clustering results becomes difficult as the number of variables considered increases, and the number of classes is not low. Sometimes concept induction methods are used to associate concepts to every class and use to be expressed as boolean expressions, easy to understand and supposedly providing good support to decision making. It has been seen that most of the concept induction algorithms priorize the compacity of the final expressions, as well as their predictive power. However, for descriptive purposes, when the meaning of classes has to be recognized and understood by the expert, this is not the best approach, since compacity directly implies elimination of redundancies or strong associations, while comprehension of the class is mainly based in understanding how variables interact among them inside the class. Here a method to induce conceptual descriptions of classes is proposed, providing non-minimal descriptions of the classes, but richer ones including the characteristics that distinguishes a class from the others, in such a way that expert can easily recognize the essence of the class, and conceptualize it on the bases of local interactions among all variables observed inside every class. This kind of interpretations provide an excellent support for later decision support systems.