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
Building Concept (Galois) Lattices from Parts: Generalizing the Incremental Methods
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
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
Reduction method for concept lattices based on rough set theory and its application
Computers & Mathematics with Applications
A framework for incremental generation of closed itemsets
Discrete Applied Mathematics
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
An incremental algorithm to construct a lattice of set intersections
Science of Computer Programming
Formal concept analysis in information science
Annual Review of Information Science and Technology
Text clustering using frequent itemsets
Knowledge-Based Systems
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
Finding association rules in semantic web data
Knowledge-Based Systems
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
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Traditional association rules consist of some redundant information. Some variants based on support and confidence measures such as non-redundant rules and minimal non-redundant rules were thus proposed to reduce the redundant information. In the past, we proposed most generalization association rules (MGARs), which were more compact than (minimal) non-redundant rules in that they considered the condition of equal or higher confidence, instead of only equal confidence. However, the execution time for generating MGARs increased with an increasing number of frequent closed itemsets. Since lattices are an effective data structure widely used in data mining, in this paper, we thus propose a lattice-based approach for fast mining most generalization association rules. Firstly, a new algorithm for building a frequent-closed-itemset lattice is introduced. After that, a theorem on pruning nodes in the lattice for rule generation is derived. Finally, an algorithm for fast mining MGARs from the lattice constructed is developed. The proposed algorithm is tested with several databases and the results show that it is more efficient than mining MGARs directly from frequent closed itemsets.