Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
A Graph-Based Approach for Discovering Various Types of Association Rules
IEEE Transactions on Knowledge and Data Engineering
A New Algorithm for Faster Mining of Generalized Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Data Mining of Association Rules and the Process of Knowledge Discovery in Databases
Industrial Conference on Data Mining: Advances in Data Mining, Applications in E-Commerce, Medicine, and Knowledge Management
Mining Generalized Association Rules Using Pruning Techniques
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
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Generalized association rules are rules that contain some background knowledge, therefore, giving a more general view of the domain. This knowledge is codified by a taxonomy set over the data set items. Many researches use taxonomies in different data mining steps to obtain generalized rules. In general, those researches reduce the obtained set by pruning some specialized rules using a subjective measure, but rarely analyzing the quality of the rules. In this context, this paper presents a quality analysis of the generalized association rules, where a different objective measure has to be used depending on the side a generalization item occurs. Based on this fact, a grouping measure was generated according to the generalization side. These measure groups can help the specialists to choose an appropriate measure to evaluate their generalized rules.