Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
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
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Approximation of Frequency Queris by Means of Free-Sets
PKDD '00 Proceedings of the 4th European Conference on Principles of 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
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
On Computing Condensed Frequent Pattern Bases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Dataless Transitions Between Concise Representations of Frequent Patterns
Journal of Intelligent Information Systems
Mining dependence rules by finding largest itemset support quota
Proceedings of the 2004 ACM symposium on Applied computing
On support thresholds in associative classification
Proceedings of the 2004 ACM symposium on Applied computing
Reducing borders of k-disjunction free representations of frequent patterns
Proceedings of the 2004 ACM symposium on Applied computing
Using Generators for Discovering Certain and Generalized Decision Rules
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Data Mining and Knowledge Discovery
Discovering Synonyms Based on Frequent Termsets
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Closures of Downward Closed Representations of Frequent Patterns
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Essential patterns: a perfect cover of frequent patterns
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Extraction of association rules based on literalsets
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Computing Implications with Negation from a Formal Context
Fundamenta Informaticae - Concept Lattices and Their Applications
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The discovery of frequent patterns has attracted a lot of attention of the data mining community. While an extensive research has been carried out for discovering positive patterns, little has been offered for discovering patterns with negation. The main hindrance to the progress of such research is huge amount of frequent patterns with negation, which exceeds the number of frequent positive patterns by orders of magnitude. In this paper, we examine properties of derivable and non-derivable patterns, including those with negated items. In particular, we establish important relationships among patterns admitting negation that have the same canonical variant. By analogy to frequent non-derivable itemsets, which constitute a concise lossless representation NDR of frequent positive patterns, we introduce frequent non-derivable literal sets lossless representation NDRL of frequent positive patterns admitting negation. Then we use the derived properties of literal sets to offer a concise representation NDIR of frequent patterns admitting negation that is built only from positive non-derivable itemsets. The relationships between the three representations are identified. The transformation of the new representations into not less concise lossless closure representations is discussed.