Fast discovery of association rules
Advances in knowledge discovery and data mining
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
A fast algorithm for building lattices
Information Processing Letters
Empirical bayes screening for multi-item associations
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
ACM SIGKDD Explorations Newsletter
Discovery in multi-attribute data with user-defined constraints
ACM SIGKDD Explorations Newsletter
Continuous queries over data streams
ACM SIGMOD Record
A partition-based approach towards constructing Galois (concept) lattices
Discrete Mathematics
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Formal Concept Analysis on Its Way from Mathematics to Computer Science
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
On Computing Condensed Frequent Pattern Bases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Evaluation of Queries with Mining Predicates
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
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
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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In the Data Mining area, discovering association rules is one of the most important task. It is well known that the number of these rules rapidly grows to be unwieldy as the frequency requirements become less strict, especially when collected data is highly correlated or dense. Since a big number of the frequent itemsets turns out to be redundant, it is sufficient to consider only the rules among closed frequent itemsets or concepts. In order to efficiently generate them, it is often essential to know the Concept Lattice, that also allows the user to better understand the relationships between the closed itemsets. We propose an incremental algorithm that mines all the closed itemsets, reading the data only once. The Concept Lattice is incrementally updated using a simple but essential structure directly connected to it. This structure allows to speed up the execution time and makes the algorithm applicable on both static and dynamic stream data and very dense datasets.