大数据下的机器学习算法综述.pdf大数据下的机器学习算法综述，介绍利用大数据做机器学习的常用算法or

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详细说明：大数据下的机器学习算法综述，介绍利用大数据做机器学习的常用算法ordan Little bootstraps Boot frap ordan 4 4.1 4.2 Kol- Tucker Memory -Efficient Tucker Decomposition MET MET densed Nearest Neighbor CNN R duced nearest neighbor RnN Ed MET ted Nearest Neighbor ENN Wahba h 10 Regularized CNN Kernel estimation RKE Robust Fast Cnn FCnn Manifold Unfolding RMU ordan Boot strap Ie 21994-2016ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net Self-organizing Map SOM SOM 16 Fast SoM fSom 1g sⅤD、 RP PCA Fuzzy Lower- pproximation -Based Fuzzy Rough Set Feature Se lection with Threshold T-FRFS Quickreduct L - FRFS Quevedo VM Simulated annealing and Genetic algo- hm saga NP askov SVM Pal SVM Minimax probability Machine ⅥPM M Incremental Kernel pca Sun Least Squares SVM Ls-svM Kim 21994-2016ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net C Q-training Based Decision tree on random forest Co -forest franco -Arcega n Yang Parallel Averaging Ir ncre Stochastic gradient Descent AsgD 120 mentally Optimized Very Fast Decision Tree 、l000 iOVFDT Information bot Benaim Extreme Learning Machine elm lg ELM 上CM ELM Havens ELMI ELM ELM FCM ELM ELM FCM Random Sampling Plus Extension FCM ELM FCM Bit-Reduced FCm M A ELM proximate Kernel FCM FCM Havens FCM k 4 21994-2016ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net 1.I|4TB10 LO I/0 Hall TB redu MapReduce 4.5 A1 priori Zhao MapReduce Apriori Means speedup、 SIzeup、 scaleup3 Papadimitriou prioraL AprioriSome Dynamic Some &i Maple Generalized Sequential Pattern clustering GSP Secuen Distributed Co-clustering DisCo tial Pattern Discovery Using Equivalence Classes Hadoop SPADE S DisCo GB g Frequent Pattern-Projected Sequential Pattern Mapreduce Ⅵ lining FreeSpan50 KNN Prefix Projected Sequential Pattern Minir PrefixSpan. SPADF Ferreira Ma for Sequential Pattern Mining MEMISP dexing M Reduce LO 2 Sequential Pattern Mining with Reg Bow best of both Worlds ular expression Constraints SPIRIT 59 BoW BoW Havens C-mean C-mean 61 Sequential Pattern GSP GSP Mining Frequent Sequences MFS sP+ Mfs SPADE ncrementa quence Mining sm 21994-2016ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net ADABOOST PL LOGITBOOST PI ISM 63 Incremen Ⅵ maPreduce tal Frequent Sequences Mining IsE ISE Mapreduce mentally Updating Sequences IUS 65 IseI 66 Latent Tradeoff between performance and Difference Dirichlet Allocation LDa TPD Collapsed Gibbs Sampling CGs Collapsed variational Bayesian CVB CPU GPU 4.6 Graphic processing Unit GPU GPU luo SVM sM、 MapReduce Compute 60% Unified device architecture CuDa Mapreduce 2008 Shim mapReduce PDMiner parallel distributed miner apReduce MapReduce MapReduce 436076-8 g Generalized Linear Aggregates Distributed En Hefeeda gine GLADE. GLADE User- Defined Aggregate UDA GLADE UDA Post P 2 21994-2016ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net 8 Brighton H Mellish C. Advances in Instance Selection for Instance Based Learning Algorithms. Data Mining and Knowledge STOVe 9 Li Yh Maguire L. Selecting Critical Patterns Based on Local geo- metrical and Statistical Information. IEEE Trans on Pattern Analy and Machine Intelligence 20|1 33 6 II 10 Angiulli F. Fast Nearesl Neighbor Condensation for L arge: Dala Sels Classification. IEEE 'Trans on Knowledge and Data Engineerin 200719111450-1464 11 Angiulli F Folino G. Distributed Nearest Neighbor-Based Conden- sation of very Large Dala Sets. IEEE Trans on Know ledge and Data Engineering200719121593-1606 12 Jordan M I. Divide-and-Conquer and Statistical Inference for Bi Data / Proc of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Dal a Mining. Reijing Chinad 2012 DOIl0.ll45/2339530.2339534 13 Kolda TG Sun J M. Scalable Tensor Decompositions for Multi-as- pect Data Mining// Proc of the &th IEEE Intemational Conference on Data Mining. Pisa Italy 2008 363-372 3 Hadoop、CLDA 14 Wahba G. Dissimilarity Dala in Statistical Model Building and Ma- / Proc of the 5 th Mathematicians. Beijing China 2012 785-809 15 Loi C iI Wang i L Zhao p l et al. Online Feature Selection for Mining big Data / Proc of the let International Workshop on B Dala Strearns and Heterogeneous Source Mining Algorithms Sys tems I aming Models and Applications. Be 201293-100 16 Sagheer A Tsuruta n Taniguchi R I et al. Fast Feature Extrac tion Approach for Multi-dimension Feature Space Problems /i Prod of the 1 8th International Conference on Pattern Recognition. Hong China2006I417-44 17 Anaraki J R Eftekhari M. Improving Fuzzy - Rough Quick Reduct 1 Labrinidis a Jagadish II V. Challenges and Opportunities with Big for Feature seler lion / Proe of the 191h Tranian Conference on Data. Proc of the vLDB Endowment 2012 5 12 2032-2033 Electrical Engineering. Tehran Iran 2011 1-6 2 Bizer C bonez p Brodie m l et al. The meaningful lse of big 18 Quevedo J R Bahamonde A Luaces O. A Simple and Eficient Data Four Perspectives- Four Challenges. ACM SIGMOD Record Method for Variable Ranking according to Their Usefulness for 201240456 3 Li G. Cheng X Q. Research Status and Scientific Thinking of Big 1578-595 Data. Bulletin of Chinese Academy of Sciences 2012 27 6 647 9 Gheyas I A Smith L S. Feature Subset Sele 657 in Ch Inest signality Domains. Pattern Recognition 2010 43 1 5-13 0 Pal M Fory G M. Fe ral Data bv svM. ieee Trans on Geoscience and remote 2012276647-657 Sensing20104852297-2307 4 Wang F Y. A Big-Data Perspeetive on Al Newton Merton and An- 21 Sun Y Todorovic s Goodison S. Local-earning Based Feature lytics Intelligence. IEEE Intelligent Systems 2012 27 5 2-4 Selection for High-Dimensional Data Analysis. IEEE Trans on Pat- 5 Simon H A. Why Should Machines Learn / Michalski R S Car term Analysis and M: chine Intelligence 2010 32 9 1610 bonell J G Mitchell Tm et al. eds. Machine Learning An Arti- 26 ficial Intelligence Approac h. Berlin Germany Springer 1983 22 Hua J P Tembe W d Dougherty E R. Performance of Feature-Se 25-37 lection Methods in the Classification of High-Dimension Data. Pat- 6 Hart P. The Condensed Nearest Neighbor Rule. IEEE Trans on In tern Recognition 2009 42 formation Theory 1968 14 3 515-516 23 Song M Yang H Siadat S H e/ al. A Comparalive StudIy of Di- 7 Gates G. The Reduced Nearest Neighbor Rule. IEEE Trans on In- mensionality Reduction 'Techniques to Enhance 'Trace Clustering formation Theorv 1972 18 3 431-433 Performances. Expert Systems with Applications 2013 40 9 21994-2016ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net 335 3722-3737 rithms for Very Large Data. IEEE Trans on Fuzzy Systems 2012 24 Lau K W Wu Q IL. Online Training of Support Vector Classifier 2061130-1146 Pattern Recognition 2003 368 1913-1920 41 Xue Z H Shen G Li J H et al. Compression-Aware 1/0 Per 25 Laskov P Gehl C Kruger S et al. Ineremental Support Vector formance Analysis for Big Data Clustering / Proc of the lst Inter Learning Analysis Implementation and Applications. Journal of national Workshop on Big Data Streams and Heterogeneous Source Machine learning research 2006 7 1909-1936 Mining Algorithms Systems Programming Models and Applica- 26 Huang K Yang H King I. ef ml. Maxi-Min Margin Machine liors. Reijing China 2012 45-52 Learning Large Margin Classifiers locally and Globally. IEEE 42 Hall L O. Exploring Big Data with Scalable Soft Clustering //Proc Trans on Neural Networks 2008 19 2 260-272 of the 6th International Conference on Soft Methods in Probability 27 Kim BJ. A Classifier for Big Data / Proc of the 6th International and Statistics. Konstanz Germany 2012 11-15 Conference on Convergence and Hybrid Information Technology 43 Zhao Wz Ma H F He Q. Parallel k-means Clustering Based on Daejeon Republic of Korea 2012 505-512 MapReduce / Proe of the I st International Conferenee on Cloud 8 Franco-Arcega A Carrasco -Ochoa J A Sanchez-Diaz omputing and Big Data. Beijing China 2009 674-679 uilding Fast Decision Trees from Large Training Sets. Intelligent 44 Papadimitriou s Sun J M. DisCo Distributed Co-elustering with Dala Analysis2012164649-64 MapReduce A Case Study towards Petabyte-Scale End-+o-End 29 Hang Y Fong S. Incrementally Optimized Decision Tree for Noisy Mining// proc of the 8th IEEE International Conference on Data Big Data / Proc of the lst International Workshop on Big Data Mining. Pisa Italy 2008 512-521 Streams and Ileterogeneous Source Mining Algorithms Systems 45 Zhang Li F F effrey J. Efficient Parallel hN\ oins for Large Programming Models and Applications. Beijing China 20 Data in MapReduce //Proe of the 15th International Conference on 30 Ben-Haim Y ToIm-Tov E. A Streaming Parallel Decision Tree Aly(- Extending Database Technology. Berlin Germany 2012 38-49 rithm. Journal of Machine Learning Rescarch 2010 11 849-872 Ferreira C RL Junior ' TC Traina A J M et aL. Clustering Very 31 Huang G B Zhu Q Y Siew C K. Extreme Leaning Machine Large Multi-dimensional Datasets with MapReduce / Proc of the Theory and Applications. N computing 2006 70 112 17th ACM SiGKdd International Conference on Knowledge discov 489-50 ery and Data Mining. San Diego USA 2011 690-698 Ensemble Based Extreme Learning Machine. 47 Havens T C Chilla R Jain A K et(/. Speedup of Fuzzy and Pus- IEEE Signal Processing Letters 2010 17 8 754-757 sibilistic Kermel c-means for Large Scale Clustering /! Proc of the 33 Heq Shang T F Zhuang F Z et al. Parallel Extreme Learning IEEE International Conference on Fuzzy Systems. Taipei China Machine for Regression Based on MapReduce. Neurocomputin 2011463-470 201310252-58 48 Niu D L Dy J C Jordan M I. Dimensionality Reduction for Spec- 34 Zhang R Lan Y Huang G B et al. Universal Approximation of Irl Cluslering // PrIx of the 141h Inlemalinnal Conferenc e on Arlifi Extreme Learning Machine with Adaptive Growth of Hidden Nodes cial Intelligence and Statistics. Fort Lauderdale USA 2011 552 IEEE Trans on Neural Networks and Learning Systems 2012 23 560 2365-371 19 Kriegel H P Kroger P Zimek A. Clustering High-Dimensional 35 Rong H J Huang g b Sundararajan n et al. Online Sequential Data A Survey on Subspace Clustering Pattern-Based Clustering Fuzzy Extreme Leaming Machine for Function Approximation and and Correlation Clustering. ACM Trans on Knowledge Discover Classification Problems. IEEE Trans on Systems Man and Cyber- from Data 2009 31 1-58 netics20093941067-1072 50 Vidal R. Subspace Clustering. IEEE Trans on Signal Processing 36 Yany Y M Wang XN Yuan X F. Bidirectional Extreme Learning 201128252-68 Machine for Regression Problem and Its Learning Effectiveness 1 Zhou Y Cheng H YuJX. Graph Clustering Based on Structural/ IEEE Trans on Neural Networks and Learning Systems 2012 23 Attribute similarities. Proc of the vldb endowment 2009 2 1718-729 37 Li M Zhou Z H. Improve Computer-Aided Diagnosis with Machine 62 Agrawal R Srikant R. Fast Algorithms for Mining Association Learning Techniques Using Undiagnosed Samples. IEEE Trans on Rules in L arge Databases //Proc of the 20th Internialionial ConIfer- Systems Man and Cybernetics 2007 37 6 1088-1098 ence on Very Large Data Bases. Santiago de Chile Chile 1994 38 Lin Yq Li F J Zhu S H et al. Large-Scale Image Classifica 487-499 lion Fasl Fealure Extraction and SVM Training// Proc of the 53 Agrawal R Srikant R. Mining Sequential Patterns // Proc of the EE Conference on Computer Vision and Pattern Recognition I l th Intemational Conference on Data Engineering. Taipei China idence usa 2011 1689-1696 19953-14 39 LingX Xue GR Dai w Y et al. Can Chinese Web Pages Be 54 Srikanth R Agrawal R. Mining Sequential Patterns Generaliza- Classified with English Data Source / Proc of the 17th Interna- tions and Performance Improvements //Proc of the 5th Internation- tional Conference on World Wide Weh. Beijing China 2008 al Conferenee on Extending DalalHse Technology Advances in Da- abase Technology. Avignon France 1996 3-17 40 Havens TC Bezdek J C Leckie C et al. Fuzzy c-means Algo- 5Zaki M J. SPADE An Efficient Algorithm for Mining Frequent Se- 21994-2016ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net 336 quences. Machine Learning 2001 42 1/2 31-60 VLDB Endowment 2012 5 12 2016-2017 56 Ilan J W Kamber M Pei J. Data Mining Concepts and Tech- 70 Zhang jb liTR Pan Y. Parallel Rough Set Based Knowledge niques. 2nd Edition. New York USA Morgan Kaufmann 2006 Acquisition Using MapReduce from Big Data// Proc of the Ist In 7 Pei j Han jw PinTo H et al. Prefixspan Mining Sequential ternational Work shop on Big Dala Streams anl Heleroyeneols Patterns Efficiently by Prefix Projected Pattern Growth / Proc of Source Mining Algorithms Systems Programming Models and he 17th International Conference on Data Engineering. Heidel Applications. Beijing China 2012 20-27 berg germany 2001 215-224 71 Heleeda M Gao F Abd-Almageed W. Distributed Approximate 58 Lin M Y Lee SY. Fast Discovery of Sequential Patterns by N Spectral Clustering for Large -Seale Datasets//Proe of the 21 st In ry Indexing /i Proe of the 4th International Conference on Data ternational ACM Symposium on Iligh-Performance Parallel and Dis- Warehousing and Knowledge Discovery. Aix-en-Provence France tributed Computing. Delft the Netherlands 2012 223-234 2002150-160 72 PHlil I Reddy C K. Scalable anl Parallel Roosting with Map Re- 59 Garofalakis M N Rastogi R Shim K. Spirit Sequential Pattern duce. IEEE Trans on Knowledge and Data Engineer ring201224 Mining with Regular Expression Constraints / Proc of the 2.5th In 101904-1916 ternational Conference on Very Large Data Bases. Edinburgh 73 Kaiser C Pozdnoukhov A. Enabling Real-lime City Sensing with Scotland1999223-234 Kernel Stream Oracles and MapReduce. Pervasive and Mobile 60 Li\ Zeng L He q et al. Parallel Implementation of Apriori Al- Computing 2013 95 708-721 gorithm Based on MapReduce //Proc of the 13th ACIs Interna- 74 Yan F XuNY QiY. Parallel Inference for Latent Dirichlet Allo- lienal Conference on Software Engineering Arlificia! Intelligence cat ion on Graphie s Processing Units / Pror of the 22nd Annual Networking and Parallel/ Distributed Computing. Kyoto Japan Conference on Neural Information Processing Systems. Whistler 2012191-200 Canada20092134-2142 61 Zhang M H Kao B Cheung DW et al. Efficient Algorithms for 75 Jung G Gnanasambandam N Mukherjee I'. Synchronous Parallel Incremental Updale of Frequent Sequenees / Proc of the 6th Pa- Processing of Big-Data Analytics Services to Optimize Performance cific-Asia Conference on Knowledge Discovery and Data Mining in Federated Clouds // Proc of the 5th Ieee International Confer hina2002186-19 ence on Cloud Computing. Hawaii USA 2012 811-818 62 Parthasarathy s Zaki M I Ogihara m et al. Incremental and In- 76 He Q Zhuang FZ li Ic et al. Parallel Implementation of clas teractive Sequence Mining // Proe of the &th International Confe ification Algorithms Based on Map reduce // Proc of the 5 th In ence on Information and Knowledye Manayement. Kansas City national Conference on Rough Sel and Knowlelge Technology. Rei- USA1999251-258 jing China 2010 655-662 63 Masseglia F Poncelet P Teisseire M. Incremental Mining of Se- 77 He Q TanQ Ma X d et al. The High-Activity Parallel Imple quential Patterns in Large Databases. Data Knowledge Engineer mentation of Data Preprocessing Based on Map Reduce ! Proc of ng200346197-121 the 5th International Conference on Rough Set and Knowledge 64 Zheng Qg Xu k Masl et al. The Algorithms of Updating Se- Technology. Beijing China 2010 646-654 quentialPatternsEb/oL.2013-05-20.http://arxiv.org/ 78 He Q Wang Q Du CY et al. A Parallel Hyper-Surface Classifi ftp /cs/papers /0203/0203027 pdf er for Iligh Dimensional Data// Proc of the 3rd International Sym- 65 Wang C Y Hong T P Tseng S S. Maintenance of Sequential Pa posium on Know ledge Acquisition and Modeling. Wuhan China erns for record deletion / Proc of the ieee international confer- 2010338-343 ence on Data Mining. San Jose USA 2001 536-541 79 He Q Ma Y L Wang Q et al. Parallel Outlier Detection Using 66 Zheng Q G Xu K Ma S L. When to Update the Sequential KD-Tree Based on MapReduce / Proc of the 3rd International Patterns of Stream Data / Proc of the 7 th pacific -sia Confer- Conference on Cloud Computing T'echnology and Science. Athens ence on Knowledge Discovery and Data Mining. Seoul Republic of Korea2003545-550 80 lle Q Wang Q Zhuang FZ et al. Parallel ClaraNs clustering 67 Upadhyaya S R. Parallel Approaches to Machine Learning-A Based on MapReduce. Energy Procedia 2011 13 3269-3279 Comprehensive Survey. Journal of Parallel and Distributed Compu- 81 Tan Q lle Q Shi Z Z. Parallel Max - Min Ant System Using Ma- ting2013733284-292 pReduce //Proc of the 3rd International Conference on Swarm In- 68 L no DJ Ding C Huang H. parallelization with Multiplicative Al- lelligenc e. Shenzhen China 2012 182-189 corithms for big Data Mining// Proc of the 12th IEEE International 82 Cheng Y Qin C J Rusu F. GLADE Big Data Analytics Made Conferenee on Data Mining. Brussels Belgium 2012 489-498 Easy!/ Proc of the ACM SIGMOD International Conference on 69 Shim K. Map Reduce Algorithms for Big Data Analysis. Proc of the Management of Data. Scottsdale USA 2012 697-700 21994-2016ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net

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