Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Measures of ruleset quality for general rules extraction methods
International Journal of Approximate Reasoning
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
Expert Systems with Applications: An International Journal
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
A new method for mining Frequent Weighted Itemsets based on WIT-trees
Expert Systems with Applications: An International Journal
Interestingness measures for classification based on association rules
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
A space-time trade off for FUFP-trees maintenance
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
A lattice-based approach for mining most generalization association rules
Knowledge-Based Systems
Association rule hiding in risk management for retail supply chain collaboration
Computers in Industry
International Journal of Intelligent Information and Database Systems
MEI: An efficient algorithm for mining erasable itemsets
Engineering Applications of Artificial Intelligence
| Hi-index | 12.06 |
There are many methods which have been developed for improving the time of mining frequent itemsets. However, the time for generating association rules were not put in deep research. In reality, if a database contains many frequent itemsets (from thousands up to millions), the time for generating association rules is more longer than the time for mining frequent itemsets. In this paper, we present a combination between lattice and hash tables for mining association rules with different interestingness measures. Our method includes two phases: (1) building frequent itemsets lattice and (2) generating interestingness association rules by combining between lattice and hash tables. To compute the measure value of a rule fast, we use the lattice to get the support of the left hand side and use hash tables to get the support of the right hand side. Experimental results show that the mining time of our method is more effective than the method that of directly mining from frequent itemsets uses hash tables only.