Algorithms for clustering data
Algorithms for clustering data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-level organization and summarization of the discovered rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
A Metric for Selection of the Most Promising Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
CHIRA---Convex Hull Based Iterative Algorithm of Rules Aggregation
Fundamenta Informaticae
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Sets of decision rules induced from data can often be very large. Such sets of rules cannot be processed efficiently. Moreover, too many rules may lead to overfitting. The number of rules can be reduced by methods like Quality-Based Filtering [1,10] returning a subset of all rules. However, such methods may produce decision models unable to match many new objects. In this paper we present a solution for reducing the number of rules by joining rules from some clusters. This leads to a smaller number of more general rules.