Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Machine Learning
ICDE '95 Proceedings of the Eleventh 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
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Knowledge discovery in databases: 10 years after
ACM SIGKDD Explorations Newsletter
Summary from the KDD-03 panel: data mining: the next 10 years
ACM SIGKDD Explorations Newsletter
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
MINI: Mining Informative Non-redundant Itemsets
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Minimum-Size Bases of Association Rules
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Using the Bibliometric Analysis to Evaluate Global Scientific Production of Data Mining Papers
DBTA '09 Proceedings of the 2009 First International Workshop on Database Technology and Applications
Guest Editorial: Global modeling using local patterns
Data Mining and Knowledge Discovery
ACM SIGKDD Explorations Newsletter
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining top−k frequent patterns without minimum support threshold
Knowledge and Information Systems
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In 1993, Rakesh Agrawal, Tomasz Imielinski and Arun N. Swami published one of the founding papers of Pattern Mining: "Mining Association Rules between Sets of Items in Large Databases". Beyond the introduction to a new problem, it introduced a new methodology in terms of resolution and evaluation. For two decades, Pattern Mining has been one of the most active fields in Knowledge Discovery in Databases. This paper provides a bibliometric survey of the literature relying on 1,087 publications from five major international conferences: KDD, PKDD, PAKDD, ICDM and SDM. We first measured a slowdown of research dedicated to Pattern Mining while the KDD field continues to grow. Then, we quantified the main contributions with respect to languages, constraints and condensed representations to outline the current directions. We observe a sophistication of languages over the last 20 years, although association rules and itemsets are so far the most studied ones. As expected, the minimal support constraint predominates the extraction of patterns with approximately 50% of the publications. Finally, condensed representations used in 10% of the papers had relative success particularly between 2005 and 2008.