Scalable Algorithms for Association Mining

Keywords

Association rule learning, Computer science, Tree traversal, Scalability, Data mining

References

• Integrating Association Rule Mining with Relational Database Systems: Alternatives and ImplicationsSunita Sarawagi, Shiby Thomas, Rakesh Agrawal — 1998
• New Algorithms for Fast Discovery of Association RulesMohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara — 1997
• Memory Placement Techniques for Parallel Association MiningSrinivasan Parthasarathy, Mohammed J. Zaki, Wei Li — 1998
• Fast Algorithms for Mining Association Rules in Large DatabasesRakesh Agrawal, Ramakrishnan Srikant — 1994
• Fast Discovery of Association RulesRakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant — 1996
• An Efficient Algorithm for Mining Association Rules in Large DatabasesAshoka Savasere, Edward Omiecinski, Shamkant B. Navathe — 1995
• Sampling Large Databases for Association RulesHannu Toivonen — 1996
• Evaluation of sampling for data mining of association rulesMohammed J. Zaki, S Parthasarathy, Wei Li — 1997
• Mining association rules: anti-skew algorithmsJun-Lin Lin, Margaret H. Dunham — 1998
• A fast distributed algorithm for mining association rulesDavid W. Cheung, Jiawei Han, Vincent Ng — 1996
• Set-oriented mining for association rules in relational databasesM.A.W. Houtsma, A. Swami — 1995
• Corrections to Bierstone's Algorithm for Generating CliquesGordon D. Mulligan, Derek G. Corneil — 1972
• Generation of Maximum Independent Sets of a Bipartite Graph and Maximum Cliques of a Circular-Arc GraphToshinobu Kashiwabara, Sumio Masuda, Kazuo Nakajima — 1992
• An effective hash-based algorithm for mining association rulesJong Soo Park, Ming-Syan Chen⋆, Philip S. Yu — 1995
• Dynamic itemset counting and implication rules for market basket dataSergey Brin, Rajeev Motwani, Jeffrey D. Ullman — 1997
• Data mining, hypergraph transversals, and machine learning (extended abstract)Dimitrios Gunopulos, Heikki Mannila, Roni Khardon — 1997
• Arboricity and Bipartite Subgraph Listing AlgorithmsDavid Eppstein — 1994
• Parallel Mining of Association RulesR. K. Agrawal, J.C. Shafer — 1996
• An efficient approach to discovering knowledge from large databasesShow-Jane Yen, A.L.P. Chen — 1996
• Scalable parallel data mining for association rulesEui-Hong Han, George Karypis, Vipin Kumar — 1997

Cited by

• LCM: An Efficient Algorithm for Enumerating Frequent Closed Item SetsTakeaki Uno, Tatsuya Asai, Yuzo Uchida — 2003
• Efficient frequent pattern miningBart Goethals — 2002
• Mining Protein Contact MapsJingjing Hu, Xiaolan Shen, Yu Shao — 2002
• ZART: A Multifunctional Itemset Mining AlgorithmLászló Szathmáry, Amedeo Napoli, Sergei O. Kuznetsov — 2007
• Formal Methods for Mining Structured ObjectsGemma C. Garriga — 2013
• Advances in Frequent Itemset Mining Implementations: Introduction to FIMI03Bart Goethals, Mohammed J. Zaki — 2003
• Understanding Filesystem Performance for Data Mining ApplicationsBouchra Bouqata, Christopher D. Carothers, Bolesław K. Szymański — 2007
• Development and application of soft computing and data mining techniques in hot dip galvanisingAndrés Sanz-García — 2013
• Mining of frequent itemsets with JoinFI-mine algorithmSupatra Sahaphong, Gumpon Sritanratana — 2011
• Mining Frequent Itemsets: a Formal UnificationSlimane Oulad-Naoui, Hadda Cherroun, Djelloul Ziadi — 2015
• Mining Interesting Patterns from Very High Dimensional Data: A Top-Down Row Enumeration ApproachHongyan Liu, Jiawei Han, Dong Xin — 2006
• AIM2: Improved implementation of AIMSagi Shporer — 2004
• Conception et validation d’une méthode de complétion des valeurs manquantes fondée sur leurs modèles d’apparitionLeila Ben Othman Amroussi — 2011
• Generalised interaction mining: probabilistic, statistical and vectorised methods in high dimensional or uncertain databasesFlorian Verhein — 2010
• Contributions to Bayesian network learning with applications to neurosciencePedro L. López-Cruz — 2013
• A Scalable Multi-Strategy Algorithm for Counting Frequent SetsSalvatore Orlando, P. Palmerini, Raffaele Perego — 2002
• ITL-MINE: Mining Frequent Itemsets More EfficientlyRaj P. Gopalan, Yudho Giri Sucahyo — 2002
• Fast Frequent Itemset Mining using Compressed Data RepresentationRaj P. Gopalan, Yudho Giri Sucahyo — 2003
• Une comparaison de certains indices de pertinence des règles d'associationMarie Plasse, Ndeye Niang Keita, Gilbert Saporta — 2006
• Toward more parallel frequent itemset mining algorithmsRobert Kessl, Pavel Tvrdı́k — 2007

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