International Journal of Man-Machine Studies
C4.5: programs for machine learning
C4.5: programs for machine learning
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Clustering Algorithms
Similarity-Driven Sampling for Data Mining
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Evaluation of sampling for data mining of association rules
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
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This paper introduces a new method for instances selection. The conceptual framework and the basic notions used by this method are those of an extended rough set theory, called α-rough set theory. In this context we formalize a notion of conflicting data, which is at the basis of a conflict normalization method used for instances selection. Extensive experiments are performed to show the efficiency and the accuracy of models built from the reduced datasets. The selection methodology and its results are discussed.