Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Parallel mining algorithms for generalized association rules with classification hierarchy
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Generalized Association Rules for Sequential and Path Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Generalized Multiple-Level Association Rules
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Discovery of Generalized Association Rules with Multiple Minimum Supports
PKDD '00 Proceedings of the 4th European Conference 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 Generalized Association Rules Using a Method of Partial-Match Retrieval
DS '99 Proceedings of the Second International Conference on Discovery Science
Mining Generalized Association Rules Using Pruning Techniques
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Rule based fuzzy classification using squashing functions
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Soft Computing and Applications
MOSSFARM: Model structure selection by fuzzy association rule mining
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
An Efficient Data Structure for Mining Generalized Association Rules
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
An interactive method for generalized association rule mining using FP-tree
Proceedings of the 2nd Bangalore Annual Compute Conference
Privacy Preserving Association Rules by Using Greedy Approach
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
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The subject of this paper is the mining of generalized association rules using pruning techniques. Given a large transaction database and a hierarchical taxonomy tree of the items, we attempt to find the association rules between the items at different levels in the taxonomy tree under the assumption that original frequent itemsets and association rules have already been generated in advance. The primary challenge of designing an efficient mining algorithm is how to make use of the original frequent itemsets and association rules to directly generate new generalized association rules, rather than re-scanning the database. In the proposed algorithms GMAR (Generalized Mining Association Rules) and GMFI (Generalized Mining Frequent Itemsets), we use join methods and/or pruning techniques to generate new generalized association rules. After several comprehensive experiments, we find that both algorithms are much better than BASIC and Cumulate algorithms, since they generate fewer candidate itemsets, and furthermore the GMAR algorithm prunes a large amount of irrelevant rules based on the minimum confidence.