Articles

  1. Mining Association Rules between Sets of Items in Large Databases, by Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Sigmod 1993
  2. Fast Algorithms for Mining Association Rules, by Rakesh Agrawal and Ramakrihnan Srikant, VLDB 94
  3. Discovery of Frequent Episodes in Event Sequences, by Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo, in Data mining and Knowledge Discovery (DMKD) vol 1, no 3, 1997
  4. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, by Jiawei Han, Jian Pei, Yiwen Yin, and Runying Mao, DMKD, vol 8, no 1, 2004
  5. Pushing Convertible Constraints in Frequent Itemset Mining, by Jian Pei, Jiawei Han, and Laks V.S. Lakshmanan in DMKD, vol 8, no 3, 2004
  6. ExAnte: Anticipated Data Reduction in Constrained Pattern Mining, by Francesco Bonchi, Fosca Giannotti, Alessio Mazzanti, and Dino Pedreschi, in PKDD 2003
  7. Is Pushing Constraints Deeply into the Mining Algorithms Really What We Want? by Jochen Hipp and Ulrich Guentzer, SIGKDD Explorations, vol 4, no 1, 2002
  8. Efficient mining of association rules using closed itemset lattices, by Nicolas Pasquier, Yves Bastide, Rafik Taouil, and Lotfi Lakhal, in Information Systems, vol 24, n0 1, 1999
  9. Non-derivable itemset mining, by Toon Calders and Bart Goethals, in DMKD vol 14, n0 1, 2007.
  10. Pattern Teams, by Arno J. Knobbe and Eric K. Y. Ho, PKDD 2006
  11. Maximally informative k-itemsets and their efficient discovery, by Arno J. Knobbe and Eric K. Y. Ho, KDD 2006
  12. The Chosen Few: On Identifying Valuable Patterns, by Bjorn Bringmann and Albrecht Zimmermann, in ICDM 2007
  13. Tiling Databases, by Floris Geerts, Bart Goethals, and Taneli Mielikäinen, in Discovery Science, 2004
  14. Direct local pattern sampling by efficient two-step random procedures, by Mario Boley, Claudio Lucchese, Daniel Paurat, and Thomas Gärtner, in KDD 2011
  15. Sampling frequent and minimal boolean patterns: theory and application in classification, by Geng Li and Mohammed J. Zaki, in Data mining and Knowledge Discovery (DMKD) 30(1): 181-225, 2016
  16. Self-Sufficient Itemsets: An approach to screening potentially interesting associations between items, by Geoffrey I. Webb, in TKDD, vol 4, no1, 2010
  17. A statistical significance testing approach to mining the most informative set of patterns, by Jefry Lijffijt, Panagiotis Papapetrou, and Kai Puolamäki
  18. Maximum entropy models and subjective interestingness: an application to tiles in binary databases, by Tijl de Bie, DMKD vol 23, no 3, 2011
  19. Subjective interestingness in exploratory data mining, by Tijl de Bie, IDA 2013
  20. Krimp: mining itemsets that compress, by Jilles Vreeken, Matthijs van Leeuwen and Arno Siebes, DMKD, vol 23, no 1, 2011