In this paper we propose the combined use of different methods to improve the data analysis process. This is obtained by combining inductive and deductive techniques. We also use different inductive techniques such as clustering algorithms, to derive data partition, and decision trees induction, characterizing classes in terms of logical rules. Inductive techniques are used for generating hypotheses from data whereas deductive techniques are used to derive knowledge and to verify hypotheses. In order to guide users in the analysis process, we have developed a system which integrates deductive tools and data mining tools such as classification algorithms, features selection algorithms, visualization tools and tools to manipulate data sets easily. The system developed is currently used in a large project whose aim is the integration of information sources containing data concerning the socio-economic aspects of Calabria and it subsequent analysis. Several experiments on the socio-economic data have shown that the combined use of different techniques improves both the comprehensibility and the accuracy of models.

Combining inductive and deductive tools for data analysis

GRECO, Sergio;
2001

Abstract

In this paper we propose the combined use of different methods to improve the data analysis process. This is obtained by combining inductive and deductive techniques. We also use different inductive techniques such as clustering algorithms, to derive data partition, and decision trees induction, characterizing classes in terms of logical rules. Inductive techniques are used for generating hypotheses from data whereas deductive techniques are used to derive knowledge and to verify hypotheses. In order to guide users in the analysis process, we have developed a system which integrates deductive tools and data mining tools such as classification algorithms, features selection algorithms, visualization tools and tools to manipulate data sets easily. The system developed is currently used in a large project whose aim is the integration of information sources containing data concerning the socio-economic aspects of Calabria and it subsequent analysis. Several experiments on the socio-economic data have shown that the combined use of different techniques improves both the comprehensibility and the accuracy of models.
Bayesian clustering, Classification; Data mining; Knowledge discovery
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/154897
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