[AIS93]

R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 207-216, Washington D.C., May 1993. [ bib | find paper in Google Scholar | find paper in Google ]

Introduces association rules and the SUPPORT-CONFIDENCE framework and an algorithm to mine large itemsets. The algorithm is sometimes called AIS after the authors initials.

[MTV94]

Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo. Efficient algorithms for discovering association rules. In Usama M. Fayyad and Ramasamy Uthurusamy, editors, AAAI Workshop on Knowledge Discovery in Databases (KDD-94), pages 181-192, Seattle, Washington, 1994. AAAI Press. [ bib | find paper in Google Scholar | find paper in Google ]

Develop similar improvements to the candidate generation as APRIORI. Itemsets with support are called covering sets. The paper also introduces sampling from the database and gives bounds for the resulting estimate of support.

[AS94]

Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules in large databases. In Jorge B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487-499, Santiago, Chile, September 1994. [ bib | find paper in Google Scholar | find paper in Google ]

Introduction of the APRIORI algorithm (the best-known algorithm; it uses a breadth-first search strategy to counting the support of itemsets). The algorithm uses an improved candidate generation function which exploits the downward closure property of support and makes it more efficient than AIS. Also an algorithm to generate synthetic transaction data is presented. Such synthetic transaction data are widely used for the evaluation and comparison of new algorithms.

[SON95]

Ashok Savasere, Edward Omiecinski, and Shamkant Navathe. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21st VLDB Conference, pages 432-443, Zurich, Switzerland, 1995. [ bib | find paper in Google Scholar | find paper in Google ]

Introduction of the PARTITION algorithm. The database is scanned only twice. For the first scan the DB is partitioned and in each partition support is counted. Then the counts are merged to generate potential large itemsets. In the second scan the potential large itemsets are counted to find the actual large itemsets.

[Toi96]

Hannu Toivonen. Sampling large databases for association rules. In VLDB '96: Proceedings of the 22th International Conference on Very Large Data Bases, pages 134-145, San Francisco, CA, USA, 1996. Morgan Kaufmann Publishers Inc. [ bib | find paper in Google Scholar | find paper in Google ]

Find frequent itemsets in a random sample of a database (that fits into main memory) and then verify the found frequent itemsets in the database.

[HGN00]

Jochen Hipp, Ulrich Güntzer, and Gholamreza Nakhaeizadeh. Algorithms for association rule mining - A general survey and comparison. SIGKDD Explorations, 2(2):1-58, 2000. [ bib | find paper in Google Scholar | find paper in Google ]

Describes the fundamentals of association rule mining and presents an systematization of existing algorithms.

[Zak00]

Mohammed J. Zaki. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3):372-390, May/June 2000. [ bib | find paper in Google Scholar | find paper in Google ]

Introduces six new algorithms combining several features (database format, the decomposition technique, and the search procedure). Includes Eclat (Equivalence CLAss Transformation), MaxEclat, Clique, MaxClique, TopDown, and AprClique. ECLAT is a well known depth-first search algorithm using set intersection.

[OLP⁺03]

Salvatore Orlando, Claudio Lucchese, Paolo Palmerini, Raffaele Perego, and Fabrizio Silvestri. kdci: a multi-strategy algorithm for mining frequent sets. In Bart Goethals and Mohammed J. Zaki, editors, FIMI'03: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, November 2003. [ bib | find paper in Google Scholar | find paper in Google ]

Introduces the kDCI algorithm.

[HPYM04]

Jiawei Han, Jian Pei, Yiwen Yin, and Runying Mao. Mining frequent patterns without candidate generation. Data Mining and Knowledge Discovery, 8:53-87, 2004. [ bib | find paper in Google Scholar | find paper in Google ]

Describes the data mining method FP-growth (frequent pattern growth) which uses an extended prefix-tree (FP-tree) structure to store the database in a compressed form. FP-growth adopts a divide-and-conquer approach to decompose both the mining tasks and the databases. It uses a pattern fragment growth method to avoid the costly process of candidate generation and testing.

[CGL04]

Frans Coenen, Graham Goulbourne, and Paul Leng. Tree structures for mining association rules. Data Mining and Knowledge Discovery, 8:25-51, 2004. [ bib | find paper in Google Scholar | find paper in Google ]

Describes how to compute PARTIAL SUPPORT COUNTS in one DB-pass and how to store them in an enumeration tree (P-Tree).

[HCXY07]

J. Han, H. Cheng, D. Xin, and X. Yan. Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery, 14(1), 2007. [ bib | find paper in Google Scholar | find paper in Google ]

Complete overview of the state-of-the art in frequent patten mining and identifies future research directions.

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