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Drift mining
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#	Item
1	MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. Albert Bifet1 , Geoff Holmes1 , Bernhard Pfahringer1 , Philipp Kranen2 , Hardy Kremer2 , Timm Jansen2 , and Thomas Seidl2 1 Add to Reading List Source URL: moa.cs.waikato.ac.nz Language: English - Date: 2014-11-07 23:43:25 Data mining Statistics Computational statistics Software Machine learning Massive Online Analysis Cluster analysis Formal sciences Data stream mining K-means clustering Concept drift Weka
2	Paired Learners for Concept Drift Stephen H. Bach and Marcus A. Maloof Department of Computer Science Georgetown University Washington, DCUSA {bach, maloof}@cs.georgetown.edu Add to Reading List Source URL: stephenbach.net Language: English - Date: 2013-06-10 18:15:07 Cognition Neuropsychology Educational psychology Education Machine learning Learning Concept drift Data mining Ensemble learning
3	Mining Data that Changes 17 July 2015 Data is Not Static • Add to Reading List Source URL: people.mmci.uni-saarland.de Language: English - Date: 2015-07-23 04:33:45 Machine learning Data mining Mathematics Algorithms Streaming algorithm Dynamic problem Matrix Concept drift Learning Multivariate statistics Non-negative matrix factorization Applied mathematics
4	LNCS 8207 - <TEX>{CD-MOA}</TEX>: Change Detection Framework for Massive Online Analysis Add to Reading List Source URL: users.ics.aalto.fi Language: English - Date: 2014-01-13 04:36:52 Data mining Statistics Change detection CUSUM Concept drift Massive Online Analysis Feature detection Application software Information
5	DENR Administrative Order NoSeptember 11, 1997 SUBJECT : Add to Reading List Source URL: www.mgb10.com Language: English - Date: 2008-06-01 22:59:08 Occupational safety and health Mining Underground mining Business Coal mining Mines and Geosciences Bureau Drift mining Mine safety Surface mining Economy United Kingdom mines and quarries regulation
6	Batch-Incremental vs. Instance-Incremental Learning in Dynamic and Evolving Data Jesse Read1 , Albert Bifet2 , Bernhard Pfahringer2 , Geoff Holmes2 1 Department of Signal Theory and Communications Add to Reading List Source URL: users.ics.aalto.fi Language: English - Date: 2012-10-25 03:54:39 Statistics Machine learning Learning Artificial intelligence Statistical classification Data mining Concept drift Massive Online Analysis K-nearest neighbors algorithm Support vector machine Stochastic gradient descent Batch processing
7	Deep Learning in Partially-labeled Data Streams Jesse Read Aalto University and HIIT Helsinki, Finland Fernando Perez-Cruz Add to Reading List Source URL: users.ics.aalto.fi Language: English - Date: 2014-12-09 08:08:51 Machine learning Learning Artificial intelligence Artificial neural networks Statistical classification Data mining Deep learning K-nearest neighbors algorithm Multi-label classification Deep belief network Restricted Boltzmann machine Concept drift
8	Synthetic Multi-label Data Streams Jesse Read, Bernhard Pfahringer, Geoff Holmes University of Waikato, Hamilton, New Zealand Feature Space Introduction Add to Reading List Source URL: users.ics.aalto.fi Language: English - Date: 2010-10-25 05:10:52 Machine learning Concept drift Data mining Slashdot Multi-label classification
9	Detecting Concept Drift in Classification Over Streaming Graphs Yibo Yao, Lawrence B. Holder School of Electrical Engineering and Computer Science Washington State University, Pullman, WA 99164 {yibo.yao, holder}@wsu.edu Add to Reading List Source URL: www.mlgworkshop.org Language: English - Date: 2016-08-14 01:22:56 Data mining Graph theory Line graph Concept drift Clique Entropy Graph
10	Vol. XXV, No.1 January, 1954 ~ui&£rzit~ of ~ri~oun ~ull£tiu Add to Reading List Source URL: azgs.az.gov Language: English - Date: 2011-10-05 02:31:49 Underground mining Mining engineering Stoping Mining Mine exploration Drift mining Shaft mining Greenside Mine