Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Towards Efficient Data Re-mining (DRM)
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Efficient incremental maintenance of frequent patterns with FP-tree
Journal of Computer Science and Technology
Top-down and bottom-up strategies for incremental maintenance of frequent patterns
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
An efficient approach for interactive mining of frequent itemsets
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
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Mining of frequent patterns has been studied popularly in data mining area. However, very little work has been done on the problem of updating mined patterns upon threshold changes, in spite of its practical benefits. When users interactively mine frequent patterns, one difficulty is how to select an appropriate minimum support threshold. So, it is often the case that they have to continuously tune the threshold. A direct way is to re-execute the mining procedure many times with varied thresholds, which is nontrivial in large database. In this paper, an efficient Extension and Re-mining algorithm is proposed for update of previously discovered frequent patterns upon threshold changes. The algorithm proposed in this paper has been implemented and its performance is compared with re-running FP-growth algorithm under different thresholds. The study shows that our algorithm is significantly faster than the latter, especially when mining long frequent patterns in large databases.