Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
TSP: Mining top-k closed sequential patterns
Knowledge and Information Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Mining top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Efficient incremental mining of top-K frequent closed itemsets
DS'07 Proceedings of the 10th international conference on Discovery science
Mining Top-k Fault Tolerant Association Rules by Redundant Pattern Disambiguation in Data Streams
ICICCI '10 Proceedings of the 2010 International Conference on Intelligent Computing and Cognitive Informatics
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
Mining top-K non-redundant association rules
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Using semantic-based association rule mining for improving clinical text retrieval
HIS'13 Proceedings of the second international conference on Health Information Science
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Mining association rules is a fundamental data mining task. However, depending on the choice of the parameters (the minimum confidence and minimum support), current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information.This is a serious problem because in practice users have limited resources for analyzing the results and thus are often only interested in discovering a certain amount of results, and fine tuning the parameters is time-consuming.To address this problem, we propose an algorithm to mine the top-k association rules, where k is the number of association rules to be found and is set by the user. The algorithm utilizes a new approach for generating association rules named rule expansions and includes several optimizations. Experimental results show that the algorithm has excellent performance and scalability, and that it is an advantageous alternative to classical association rule mining algorithms when the user want to control the number of rules generated.