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 association rules using inverted hashing and pruning
Information Processing Letters
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Efficient breadth-first mining of frequent pattern with monotone constraints
Knowledge and Information Systems
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets
Data Mining and Knowledge Discovery
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
CBAR: an efficient method for mining association rules
Knowledge-Based Systems
A compress-based association mining algorithm for large dataset
ICCS'03 Proceedings of the 2003 international conference on Computational science
Towards personalized recommendation by two-step modified Apriori data mining algorithm
Expert Systems with Applications: An International Journal
Index-BitTableFI: An improved algorithm for mining frequent itemsets
Knowledge-Based Systems
FIUT: A new method for mining frequent itemsets
Information Sciences: an International Journal
A new logic correlation rule for HIV-1 protease mutation
Expert Systems with Applications: An International Journal
Advanced Matrix Algorithm (AMA): reducing number of scans for association rule generation
International Journal of Business Intelligence and Data Mining
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Distributed BitTable multi-agent association rules mining algorithm
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
Expert Systems with Applications: An International Journal
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
Efficient colossal pattern mining in high dimensional datasets
Knowledge-Based Systems
Efficient analysis of transactions to improve web recommendations
Proceedings of the 13th International Conference on Interacción Persona-Ordenador
Parallel frequent itemset mining using systolic arrays
Knowledge-Based Systems
ShrFP-tree: an efficient tree structure for mining share-frequent patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
An efficient method for mining frequent itemsets with double constraints
Engineering Applications of Artificial Intelligence
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Mining frequent itemsets in transaction databases, time-series databases and many other kinds of databases is an important task and has been studied popularly in data mining research. The problem of mining frequent itemsets can be solved by constructing a candidate set of itemsets first, and then, identifying those itemsets that meet the frequent itemset requirement within this candidate set. Most of the previous research mainly focuses on pruning to reduce the candidate itemsets amounts and the times of scanning databases. However, many algorithms adopt an Apriori-like candidate itemsets generation and support count approach that is the most time-wasted process. To address this issue, the paper proposes an effective algorithm named as BitTableFI. In the algorithm, a special data structure BitTable is used horizontally and vertically to compress database for quick candidate itemsets generation and support count, respectively. The algorithm can also be used in many Apriori-like algorithms to improve the performance. Experiments with both synthetic and real databases show that BitTableFI outperforms Apriori and CBAR which uses ClusterTable for quick support count.