Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
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
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
CloseMiner: Discovering Frequent Closed Itemsets Using Frequent Closed Tidsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
Frequent Closed Itemset Mining Using Prefix Graphs with an Efficient Flow-Based Pruning Strategy
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Index-BitTableFI: An improved algorithm for mining frequent itemsets
Knowledge-Based Systems
The Pre-FUFP algorithm for incremental mining
Expert Systems with Applications: An International Journal
Maintenance of fast updated frequent pattern trees for record deletion
Computational Statistics & Data Analysis
An effective mining approach for up-to-date patterns
Expert Systems with Applications: An International Journal
A decremental algorithm of frequent itemset maintenance for mining updated databases
Expert Systems with Applications: An International Journal
An efficient and effective association-rule maintenance algorithm for record modification
Expert Systems with Applications: An International Journal
Mining minimal non-redundant association rules using frequent itemsets lattice
International Journal of Intelligent Systems Technologies and Applications
Interestingness measures for association rules: Combination between lattice and hash tables
Expert Systems with Applications: An International Journal
An adaptive approach to mining frequent itemsets efficiently
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
Mining frequent itemsets in large databases: The hierarchical partitioning approach
Expert Systems with Applications: An International Journal
An efficient method for mining frequent itemsets with double constraints
Engineering Applications of Artificial Intelligence
MEI: An efficient algorithm for mining erasable itemsets
Engineering Applications of Artificial Intelligence
Incrementally building frequent closed itemset lattice
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
Frequent closed itemsets (FCI) play an important role in pruning redundant rules fast. Therefore, a lot of algorithms for mining FCI have been developed. Algorithms based on vertical data formats have some advantages in that they require scan databases once and compute the support of itemsets fast. Recent years, BitTable (Dong & Han, 2007) and IndexBitTable (Song, Yang, & Xu, 2008) approaches have been applied for mining frequent itemsets and results are significant. However, they always use a fixed size of Bit-Vector for each item (equal to number of transactions in a database). It leads to consume more memory for storage Bit-Vectors and the time for computing the intersection among Bit-Vectors. Besides, they only apply for mining frequent itemsets, algorithm for mining FCI based on BitTable is not proposed. This paper introduces a new method for mining FCI from transaction databases. Firstly, Dynamic Bit-Vector (DBV) approach will be presented and algorithms for fast computing the intersection between two DBVs are also proposed. Lookup table is used for fast computing the support (number of bits 1 in a DBV) of itemsets. Next, subsumption concept for memory and computing time saving will be discussed. Finally, an algorithm based on DBV and subsumption concept for mining frequent closed itemsets fast is proposed. We compare our method with CHARM, and recognize that the proposed algorithm is more efficient than CHARM in both the mining time and the memory usage.