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. 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 the analysis process, we have developed a system which integrates deductive tools, data mining tools (such as classification algorithms and features selection algorithms), visualization tools and tools for the easy manipulation of data sets. 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 the analysis of the integrated data. Several experiments on socio-economic indicators of Calabrian cities have shown that the combined use of different techniques improves both the comprehensibility and the accuracy of models.
Work partially supported by a MURST grant under the projects “Data-X” and “Piano Telematico Calabria”
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Greco, S., Masciari, E., Pontieri, L. (2001). Combining Different Data Mining Techniques to Improve Data Analysis. In: Larsen, H.L., Andreasen, T., Christiansen, H., Kacprzyk, J., Zadrożny, S. (eds) Flexible Query Answering Systems. Advances in Soft Computing, vol 7. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1834-5_42
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DOI: https://doi.org/10.1007/978-3-7908-1834-5_42
Publisher Name: Physica, Heidelberg
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