C4.5: programs for machine learning
C4.5: programs for machine learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Integrating inductive and deductive reasoning for data mining
Advances in knowledge discovery and data mining
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
A Tightly-Coupled Architecture for Data Mining
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Applying Data Mining Techniques to a Health Insurance Information System
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Unbiased assessment of learning algorithms
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
A Model and a Toolkit for Supporting Incremental Data Warehouse Construction
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Integrating induction and deduction for noisy data mining
Information Sciences: an International Journal
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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 its 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.