Fast discovery of association rules
Advances in knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Covering with Reducts - A Fast Algorithm for Rule Generation
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Comparison of lazy classification algorithms based on deterministic and inhibitory decision rules
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Transactions on rough sets XII
Difference-similitude matrix in text classification
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
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The existing rough set based methods are not applicable for large data set because of the high time and space complexity and the lack of scalability. We present a classification method, which is equivalent to rough set based classification methods, but is scalable and applicable for large data sets. The proposed method is based on lazy learning idea [2] and Apriori algorithm for sequent item-set approaches [1]. In this method the set of decision rules matching the new object is generated directly from training set. Accept classification task, this method can be used for adaptive rule generation system where data is growing up in time.