Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Knowledge about multi-dimensional frequent patterns is interesting and useful. The classic frequent pattern mining algorithms based on a uniform minimum support, such as Apriori and FP-growth, either miss interesting patterns of low support or suffer from the bottleneck of itemset generation. Other frequent pattern mining algorithms, such as Adaptive Apriori, though taking various supports, focus mining at a single abstraction level. Furthermore, as an Apriori-based algorithm, the efficiency of Adaptive Apriori suffers from the multiple database scans. In this paper, we extend FP-growth to attack the problem of multidimensional frequent pattern mining. The algorithm Ada-FP, which stands for Adaptive FP-growth. The efficiency of the Ada-FP is guaranteed by the high scalability of FP-growth. To increase the effectiveness, the Ada-FP pushes various support constraints into the mining process. We show that the Ada-FP is more flexible at capturing desired knowledge than previous Algorithm.