Rdb/VMS: developing the data warehouse
Rdb/VMS: developing the data warehouse
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 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
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
FARM: A Framework for Exploring Mining Spaces with Multiple Attributes
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Research on Association Rules Mining AlgorithmWith Item Constraints
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Extending the state-of-the-art of constraint-based pattern discovery
Data & Knowledge Engineering
GAM: a guidance enabled association mining environment
International Journal of Business Intelligence and Data Mining
Mining Frequent Patterns with Item, Aggregation, and Cardinality Constraints
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Mining association rules with multi-dimensional constraints
Journal of Systems and Software
Hi-index | 0.00 |
In this paper, the problem of constraint-based pattern discovery is investigated. By allowing more user-specified constraints other than traditional rule measurements, e.g., minimum support and minimum confidence, research work on this topic endeavoured to reflect real interest of analysts and relieve them from the overabundance of rules. Surprisingly, very little research has been conducted to deal with multiple types of constraints. In our previous work, we have studied this problem, specifically focusing on three different types of constraints, and an efficient apriori-like algorithm, called MCFP, is proposed. In this paper, we propose a new algorithm called MCFPTree, which is based on a tree structure for keeping frequent patterns without suffering from the problem of candidate itemsets generation. Experimental results show that our MCFPTree algorithm is significantly faster than MCFP and an intuitive method FP-Growth+, i.e., post-processing the frequent patterns generated by FP-Growth, against user-specified constraints.