Fast discovery of association rules
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Closed Set Based Discovery of Representative Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Dataless Transitions Between Concise Representations of Frequent Patterns
Journal of Intelligent Information Systems
Data Mining and Knowledge Discovery
Theoretical bounds on the size of condensed representations
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Extraction of frequent few-overlapped monotone DNF formulas with depth-first pruning
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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Frequent patterns are often used for discovery of several types of knowledge such as association rules, episode rules, sequential patterns, and clusters. Since the number of frequent itemsets is usually huge, several lossless representations have been proposed. Frequent closed itemsets and frequent generators are the most useful representations from application point of view. Discovery of closed itemsets requires prior discovery of generators. Generators however are usually discovered directly from the data set. In this paper we will prove experimentally that it is more beneficial to compute the generators representation in two phases: 1) by extracting the generalized disjunction-free generators representation from the database, and 2) by transforming this representation into the frequent generators representation. The respective algorithm of transitioning from one representation to the other is proposed.