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
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Dynamic Miss-Counting Algorithms: Finding Implication and Similarity Rules with Confidence Pruning
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Using Condensed Representations for Interactive Association Rule Mining
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
IEEE Transactions on Knowledge and Data Engineering
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Application-Independent Feature Construction from Noisy Samples
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Mining fault-tolerant item sets using subset size occurrence distributions
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Theoretical bounds on the size of condensed representations
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Feature construction and δ-free sets in 0/1 samples
DS'06 Proceedings of the 9th international conference on Discovery Science
Efficient mining of association rules based on formal concept analysis
Formal Concept Analysis
From local pattern mining to relevant bi-cluster characterization
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
The hows, whys, and whens of constraints in itemset and rule discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Boolean formulas and frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining frequent δ-free patterns in large databases
DS'05 Proceedings of the 8th international conference on Discovery Science
Iterative bayesian network implementation by using annotated association rules
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Constraint-Based mining of fault-tolerant patterns from boolean data
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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Given a large collection of transactions containing items, a basic common data mining problem is to extract the so-called frequent itemsets (i.e., set of items appearing in at least a given number of transactions). In this paper, we propose a structure called free-sets, from which we can approximate any itemset support (i.e., the number of transactions containing the itemset) and we formalize this notion in the framework of Ɛ-adequate representation [10]. We show that frequent free-sets can be efficiently extracted using pruning strategies developed for frequent item-set discovery, and that they can be used to approximate the support of any frequent itemset. Experiments run on real dense data sets show a significant reduction of the size of the output when compared with standard frequent itemsets extraction. Furthermore, the experiments show that the extraction of frequent free-sets is still possible when the extraction of frequent itemsets becomes intractable. Finally, we show that the error made when approximating frequent itemset support remains very low in practice.