Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient mining of association rules using closed itemset lattices
Information Systems
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining with Cover and Extension Operators
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Inducing Theory for the Rule Set
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Closed set based discovery of maximal covering rules
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - Intelligent information systems
An efficient framework for mining flexible constraints
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
New exact concise representation of rare correlated patterns: application to intrusion detection
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Many knowledge discovery problems can be solved efficiently by means of frequent patterns present in the database. Frequent patterns are useful in the discovery of association rules, episode rules, sequential patterns and clusters. Nevertheless, there are cases when a user is not allowed to access the database and can deal only with a provided fraction of knowledge. Still, the user hopes to find new interesting relationships. In the paper, we offer a new method of inferring new knowledge from the provided fraction of patterns. Two new operators of shrinking and extending patterns are introduced. Surprisingly, a small number of patterns can be considerably extended into the knowledge base. Pieces of the new knowledge can be either exact or approximate. In the paper, we introduce a concise lossless representation of the given and derivable patterns. The introduced representation is exact regardless the character of the derivable patterns it represents. We show that the discovery process can be carried out mainly as an iterative transformation of the patterns representation.