Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Discovering knowledge from large databases using prestored information
Information Systems
TBAR: An efficient method for association rule mining in relational databases
Data & Knowledge Engineering
Mining association rules using inverted hashing and pruning
Information Processing Letters
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth 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
A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
Algorithms for mining association rules in bag databases
Information Sciences—Informatics and Computer Science: An International Journal
Detecting and resolving policy misconfigurations in access-control systems
Proceedings of the 13th ACM symposium on Access control models and technologies
A new logic correlation rule for HIV-1 protease mutation
Expert Systems with Applications: An International Journal
Detecting and resolving policy misconfigurations in access-control systems
ACM Transactions on Information and System Security (TISSEC)
Advanced Matrix Algorithm (AMA): reducing number of scans for association rule generation
International Journal of Business Intelligence and Data Mining
A new smooth support vector machine
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Predictive combinations of monitor alarms preceding in-hospital code blue events
Journal of Biomedical Informatics
| Hi-index | 0.00 |
Finding association rules is an important data mining problem and can be derived based on mining large frequent candidate sets. In this paper, a new algorithm for efficient generating large frequent candidate sets is proposed, which is called Matrix Algorithm. The algorithm generates a matrix which entries 1 or 0 by passing over the cruel database only once, and then the frequent candidate sets are obtained from the resulting matrix. Finally association rules are mined from the frequent candidate sets. Numerical experiments and comparison with the Apriori Algorithm are made on 4 randomly generated test problems with small, middle and large sizes. Experiments results confirm that the proposed algorithm is more effective than Apriori Algorithm.