Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Building Concept (Galois) Lattices from Parts: Generalizing the Incremental Methods
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
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
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
A framework for incremental generation of closed itemsets
Discrete Applied Mathematics
Mining closed itemsets in data stream using formal concept analysis
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
Expert Systems with Applications: An International Journal
A lattice-based approach for mining most generalization association rules
Knowledge-Based Systems
Relational concept analysis: mining concept lattices from multi-relational data
Annals of Mathematics and Artificial Intelligence
| Hi-index | 12.05 |
A concept lattice is an ordered structure between concepts. It is particularly effective in mining association rules. However, a concept lattice is not efficient for large databases because the lattice size increases with the number of transactions. Finding an efficient strategy for dynamically updating the lattice is an important issue for real-world applications, where new transactions are constantly inserted into databases. To build an efficient storage structure for mining association rules, this study proposes a method for building the initial frequent closed itemset lattice from the original database. The lattice is updated when new transactions are inserted. The number of database rescans over the entire database is reduced in the maintenance process. The proposed algorithm is compared with building a lattice in batch mode to demonstrate the effectiveness of the proposed algorithm.