Association rule learning

Results: 289



#Item
21Assessing Data Mining Results via Swap Randomization Aristides Gionis Heikki Mannila  Taneli Mielikainen

Assessing Data Mining Results via Swap Randomization Aristides Gionis Heikki Mannila Taneli Mielikainen

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Source URL: www.cs.uoi.gr

Language: English - Date: 2006-06-19 10:07:06
22ISSN No: International Journal & Magazine of Engineering, Technology, Management and Research A Monthly Peer Reviewed Open Access International e-Journal

ISSN No: International Journal & Magazine of Engineering, Technology, Management and Research A Monthly Peer Reviewed Open Access International e-Journal

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Source URL: www.ijmetmr.com

Language: English - Date: 2014-12-09 09:23:28
23December 3, :30 WSPC/INSTRUCTION FILE

December 3, :30 WSPC/INSTRUCTION FILE

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Source URL: www.patternsthatmatter.org

Language: English - Date: 2014-12-31 10:52:18
24Package ‘arules’ August 6, 2016 VersionDateTitle Mining Association Rules and Frequent Itemsets Description Provides the infrastructure for representing,

Package ‘arules’ August 6, 2016 VersionDateTitle Mining Association Rules and Frequent Itemsets Description Provides the infrastructure for representing,

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Source URL: cran.r-project.org

Language: English - Date: 2016-08-06 16:33:45
25Siren: An Interactive Tool for Mining and Visualizing Geospatial Redescriptions [Demo] Esther Galbrun  Pauli Miettinen

Siren: An Interactive Tool for Mining and Visualizing Geospatial Redescriptions [Demo] Esther Galbrun Pauli Miettinen

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Source URL: people.mpi-inf.mpg.de

Language: English - Date: 2012-07-02 11:36:42
26Efficient Discovery of the Most Interesting Associations GEOFFREY I. WEBB, Monash University JILLES VREEKEN, University of Antwerp Self-sufficient itemsets have been proposed as an effective approach to summarizing the k

Efficient Discovery of the Most Interesting Associations GEOFFREY I. WEBB, Monash University JILLES VREEKEN, University of Antwerp Self-sufficient itemsets have been proposed as an effective approach to summarizing the k

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Source URL: eda.mmci.uni-saarland.de

Language: English - Date: 2014-07-09 04:28:40
27Introduction to arules – A computational environment for mining association rules and frequent item sets

Introduction to arules – A computational environment for mining association rules and frequent item sets

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Source URL: cran.r-project.org

Language: English - Date: 2016-08-06 16:33:45
28Interactive Exploration of Larger Pattern Collections: A Case Study on a Cocktail Dataset

Interactive Exploration of Larger Pattern Collections: A Case Study on a Cocktail Dataset

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Source URL: poloclub.gatech.edu

Language: English - Date: 2014-08-15 04:33:16
29Exploiting Implicit Item Relationships for Recommender Systems Zhu Sun, Guibing Guo∗ , and Jie Zhang School of Computer Engineering, Nanyang Technological University, Singapore ∗ School of Information Systems, Singap

Exploiting Implicit Item Relationships for Recommender Systems Zhu Sun, Guibing Guo∗ , and Jie Zhang School of Computer Engineering, Nanyang Technological University, Singapore ∗ School of Information Systems, Singap

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Source URL: www.ntu.edu.sg

Language: English - Date: 2015-04-13 01:06:23
30AMIE: Association Rule Mining under Incomplete Evidence in Ontological Knowledge Bases

AMIE: Association Rule Mining under Incomplete Evidence in Ontological Knowledge Bases

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Source URL: www2013.wwwconference.org

Language: English - Date: 2014-07-21 08:47:06