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
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
An efficient approach to discovering knowledge from large databases
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Mining Generalized Association Rules for Sequential and Path Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Generalized Association Rules with Multiple Minimum Supports
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Data Mining of Generalized Association Rules Using a Method of Partial-Match Retrieval
DS '99 Proceedings of the Second International Conference on Discovery Science
An Efficient Data Mining Technique for Discovering Interesting Association Rules
DEXA '97 Proceedings of the 8th International Workshop on Database and Expert Systems Applications
Mining Generalized Association Rules Using Pruning Techniques
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
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
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
A Cost-Efficient and Versatile Sanitizing Algorithm by Using a Greedy Approach
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Effective product assignment based on association rule mining in retail
Expert Systems with Applications: An International Journal
Exploring fuzzy ontologies in mining generalized association rules
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Utility-based association rule mining: A marketing solution for cross-selling
Expert Systems with Applications: An International Journal
Misleading Generalized Itemset discovery
Expert Systems with Applications: An International Journal
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The goal of this paper is to use an efficient data structure to find the generalized association rules between the items at different levels in a taxonomy tree under the assumption that the original frequent itemsets and association rules were 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 rescanning the database. In the paper, we used an efficient data structure called the frequent closed enumeration table (FCET) to store the relevant information. It stores only maximal itemsets, and can be used to derive the information of the subset itemsets in a maximal itemset through a hash function. In the proposed algorithms GMAR and GMFI, we used join methods and/or pruning techniques to generate new generalized association rules. Through several comprehensive experiments, we found that both algorithms are much better than BASIC and Cumulate algorithms also using the efficient data structure (FCET), owing to fewer candidate itemsets generated by GMAR and GMFI. Furthermore, the GMAR algorithm prunes a large amount of irrelevant rules based on the minimum confidence.