Online association rule mining
SIGMOD '99 Proceedings of the 1999 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
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth 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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
An Adaptive Algorithm for Incremental Mining of Association Rules
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
CrystalBall: a framework for mining variants of association rules
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
A Support-Ordered Trie for Fast Frequent Itemset Discovery
IEEE Transactions on Knowledge and Data Engineering
The RSO Algorithm for Reducing Number of Set Operations in Association Rule Mining
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
A generalized parallel algorithm for frequent itemset mining
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
On pushing weight constraints deeply into frequent itemset mining
Intelligent Data Analysis
Constructing complete FP-Tree for incremental mining of frequent patterns in dynamic databases
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Efficient algorithm for mining correlated Protein-DNA binding cores
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
| Hi-index | 0.00 |
Association rule mining is a well-researched area where many algorithms have been proposed to improve the speed of mining. In this paper, we propose an innovative algorithm called Rapid Association Rule Mining (RARM) to once again break this speed barrier. It uses a versatile tree structure known as the Support-Ordered Trie Itemset (SOTrieIT) structure to hold pre-processed transactional data. This allows RARM to generate large 1-itemsets and 2-itemsets quickly without scanning the database and without candidate 2-itemset generation. It achieves significant speed-ups because the main bottleneck in association rule mining using the Apriori property is the generation of candidate 2-itemsets. RARM has been compared with the classical mining algorithm Apriori and it is found that it outperforms Apriori by up to two orders of magnitude (100 times), much more than what recent mining algorithms are able to achieve.