An incremental concept formation approach for learning from databases
Theoretical Computer Science - Special issue on formal methods in databases and software engineering
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Efficient mining of association rules using closed itemset lattices
Information Systems
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient algorithm to update large itemsets with early pruning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Quantifying the utility of the past in mining large databases
Information Systems
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A partition-based approach towards constructing Galois (concept) lattices
Discrete Mathematics
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On the Complexity of Generating Maximal Frequent and Minimal Infrequent Sets
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Mining Bases for Association Rules Using Closed Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
ICDE '01 Proceedings of the 17th International Conference on Data Engineering
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
A framework for incremental generation of closed itemsets
Discrete Applied Mathematics
A framework for incremental generation of closed itemsets
Discrete Applied Mathematics
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
A hybrid index structure for set-valued attributes using itemset tree and inverted list
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Mining closed itemsets in data stream using formal concept analysis
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
The augmented itemset tree: a data structure for online maximum frequent pattern mining
DS'11 Proceedings of the 14th international conference on Discovery science
A completeness analysis of frequent weighted concept lattices and their algebraic properties
Data & Knowledge Engineering
A lattice-based approach for mining most generalization association rules
Knowledge-Based Systems
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
Incrementally building frequent closed itemset lattice
Expert Systems with Applications: An International Journal
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Association rule mining from a transaction database (TDB) requires the detection of frequently occurring patterns, called frequent itemsets (FIs), whereby the number of FIs may be potentially huge. Recent approaches for FI mining use the closed itemset paradigm to limit the mining effort to a subset of the entire FI family, the frequent closed itemsets (FCIs). We show here how FCIs can be mined incrementally yet efficiently whenever a new transaction is added to a database whose mining results are available. Our approach for mining FIs in dynamic databases relies on recent results about lattice incremental restructuring and lattice construction. The fundamentals of the incremental FCI mining task are discussed and its reduction to the problem of lattice update, via the CI family, is made explicit. The related structural results underlie two algorithms for updating the set of FCIs of a given TDB upon the insertion of a new transaction. A straightforward method searches for necessary completions throughout the entire CI family, whereas a second method exploits lattice properties to limit the search to CIs which share at least one item with the new transaction. Efficient implementations of the parsimonious method is discussed in the paper together with a set of results from a preliminary study of the method's practical performances.