Keywords
knowledge discovery and data mining, hierarchical clustering, class interpretation, induction rules, wastewater treatment plant
Start Date
1-7-2008 12:00 AM
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 in Environmental Domains is applied to the interpretation of previously identified classes characterizing situations on a Waste Water Treatment Plant (WWTP). The particularity of the method is that it provides an interpretation of a partition previously obtained on an ill-structured domain, on the basis of a previous hierarchical clustering. The methodology uses some statistical tools (such as the multiple boxplot) together with artificial intelligent tools (as 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 concepts for later identification of classes. In this paper the usefulness of CCEC for building domain theories as models for supporting later decision-making is addressed and contrasted with interpretation provided by experts.
Automatic generation of conceptual descriptions of classifications in Environmental Domains
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 in Environmental Domains is applied to the interpretation of previously identified classes characterizing situations on a Waste Water Treatment Plant (WWTP). The particularity of the method is that it provides an interpretation of a partition previously obtained on an ill-structured domain, on the basis of a previous hierarchical clustering. The methodology uses some statistical tools (such as the multiple boxplot) together with artificial intelligent tools (as 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 concepts for later identification of classes. In this paper the usefulness of CCEC for building domain theories as models for supporting later decision-making is addressed and contrasted with interpretation provided by experts.
