Item Sets

This list of references is preliminary and subject to future extensions.

Surveys

• Survey on Frequent Itemset Mining
  Ferenc Bodon
  Technical Report, Budapest University of Technology and Economics, Budapest, Hungary 2006
  A fairly new and extensive survey that covers the basic algorithms, but also some refined data structures. However, it is still under construction and thus in places not yet quite "smooth".
• Survey on Frequent Pattern Mining
  Bart Goethals
  Even though the title refers to frequent pattern mining, this survey is restricted to frequent item set mining. This, however, it covers very well, describing the most important algorithms and providing a comparison.
• Frequent Closed Itemset Based Algorithms: A Thorough Structural and Analytical Survey
  S. Ben Yahia, T. Hamrouni, and E. Mephu Nguito
  SIGKDD Explorations 8(1):93-104.
  ACM Press, New York, NY, USA 2006

Specific Articles (in chronological order)

• Mining Association Rules between Sets of Items in Large Databases
  R. Agrawal, T Imielinski, and A. Swami
  Proc. ACM SIGMOD Int. Conf. on Management of Data, 207-216
  ACM Press, New York, NY, USA 1993
  The seminal paper on association rule mining that introduced the problem of mining frequent item sets and association rules as well as the first algorithm for this task (AIS)
• Fast Algorithms for Mining Association Rules
  R. Agrawal and R. Srikant
  Proc. 20th Int. Conf. on very Large Databases (VLDB 1994, Santiago de Chile), 487-499
  Morgan Kaufmann, San Mateo, CA, USA 1994
  This paper introduced the Apriori algorithm, which is still the most widely known frequent item set/association rule mining algorithm.
• Mining Generalized Association Rules
  R. Srikant and R. Agrawal
  Proc. 21st Int. Conf. on very Large Databases (VLDB 1995, Zurich, Switzerland), 407-419
  Morgan Kaufmann, San Mateo, CA, USA 1995
• Fast Discovery of Association Rules
  R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A.I. Verkamo
  Advances in Knowledge Discovery and Data Mining, 307-328.
  AAAI Press, Menlo Park, CA, USA 1996
• New Algorithms for Fast Discovery of Association Rules
  M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li
  Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining (KDD 1997, Newport Beach, CA), 283-296
  AAAI Press, Menlo Park, CA, USA 1997
  This paper introduced the Eclat algorithm, the second most widely known algorithm.
• Mining Frequent Patterns without Candidate Generation
  J. Han, J Pei, and Y. Yin
  Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2000, Dallas, TX), 1-12
  ACM Press, New York, NY, USA 2000
  This paper introduced the FP-growth algorithm, whose data structure, the FP-tree, is an elegant way of organizing the data set.
• H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
  J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang
  Proc. 1st IEEE Int. Conf. on Data Mining (ICDM 2001), 441-448.
  IEEE Press, Piscataway, NJ, USA 2001
  This paper introduced H-Mine, an improvement of the FP-growth algorithm for sparse data sets.
• LPMiner: An Algorithm for Finding Frequent Itemsets Using Length Decreasing Support Constraint
  M. Seno and G. Karypis
  Proc. 1st IEEE Int. Conf. on Data Mining (ICDM 2001)
  IEEE Press, Piscataway, NJ, USA 2001
• LCM: An Efficient Algorithm for Enumerating Frequent Closed Item Sets
  T. Uno, T. Asai, Y. Uchida, and H. Arimura
  Proc. Workshop on Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL)
  CEUR Workshop Proceedings 90, TU Aachen, Germany 2003
• MAFIA: A Performance Study of Mining Maximal Frequent Itemsets
  D. Burdick, M. Calimlim, J. Flannick, J. Gehrke, and T. Yiu
  Proc. Workshop on Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL) CEUR Workshop Proceedings 90, TU Aachen, Germany 2003
• Benchmarking Frequent Itemset Mining Algorithms: From Measurement to Analysis
  Proc. Open Source Data Mining on Frequent Pattern Mining Implementations (OSDM 2005 at ACM SIGKDD 2005, Chicago, IL), 36-45
  ACM Press, New York, NY, USA 2005