Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh 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
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
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
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Association Mining in Large Databases: A Re-examination of Its Measures
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Mining minimal non-redundant association rules using frequent itemsets lattice
International Journal of Intelligent Systems Technologies and Applications
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Multilevel knowledge in transactional databases plays a significant role in our real-life market basket analysis. Many researchers have mined the hierarchical association rules and thus proposed various approaches. However, some of the existing approaches produce many multilevel and cross-level association rules that fail to convey quality information. From these large number of redundant association rules, it is extremely difficult to extract any meaningful information. There also exist some approaches that mine minimal association rules, but these have many shortcomings due to their naive-based approaches. In this paper, we have focused on the need for generating hierarchical minimal rules that provide maximal information. An algorithm has been proposed to derive minimal multilevel association rules and cross-level association rules. Our work has made significant contributions in mining the minimal cross-level association rules, which express the mixed relationship between the generalized and specialized view of the transaction itemsets. We are the first to design an efficient algorithm using a closed itemset lattice-based approach, which can mine the most relevant minimal cross-level association rules. The parent-child relationship of the lattices has been exploited while mining cross-level closed itemset lattices. We have extensively evaluated our proposed algorithm's efficiency using a variety of real-life datasets and performing a large number of experiments. The proposed algorithm has outperformed the existing related work significantly during the pervasive performance comparison.