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K-means clustering
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#	Item
891	Microsoft Word - OAC Working Paper.doc Add to Reading List Source URL: www.geog.leeds.ac.uk Language: English - Date: 2012-08-14 11:24:35 Hierarchical clustering Network analysis K-means clustering Computer cluster Statistics Computing Cluster analysis
892	Here is a step by step guide for preparing the K-Means algorithm for submission to flowCAP using the R scripting language: #Parse the CSV file cells=read.csv(’GvHD/CSV/001.csv’) #Use kmeans to find 4 populations km=k Add to Reading List Source URL: flowcap.flowsite.org Language: English - Date: 2010-08-30 23:36:43 Subroutines Comma-separated values Graft-versus-host disease Printf format string K-means clustering Mkdir Main function D Computing Software engineering Computer programming
893	A Large Scale Clustering Scheme for Kernel K-Means Rong Zhang and Alexander I. Rudnicky School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA {rongz,[removed]} Abstract Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2002-10-11 13:49:41 Multivariate statistics Hilbert space K-means clustering Positive-definite kernel Linear map Cluster analysis K-nearest neighbor algorithm Sheaf Kernel Statistics Mathematics Machine learning
894	rEMM: Extensible Markov Model for Data Stream Clustering in R Michael Hahsler Margaret H. Dunham Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-01-14 10:43:27 Data analysis Data mining Geostatistics Data stream clustering BIRCH K-medians clustering K-means clustering K-medoids DBSCAN Statistics Cluster analysis Multivariate statistics
895	Microsoft Word - dmtutorialreport.doc Add to Reading List Source URL: dimacs.rutgers.edu Language: English - Date: 2006-05-11 09:13:06 Data mining Unsupervised learning Statistical classification K-means clustering Naive Bayes classifier Bayesian network Expectation–maximization algorithm Computational epidemiology Supervised learning Statistics Machine learning Cluster analysis
896	NBER WORKING PAPER SERIES A CLUSTER-GRID PROJECTION METHOD: SOLVING PROBLEMS WITH HIGH DIMENSIONALITY Kenneth L. Judd Lilia Maliar Add to Reading List Source URL: stanford.edu Language: English - Date: 2010-08-27 18:08:38 Numerical analysis Data analysis Multivariate statistics Cluster analysis Numerical integration Ergodic theory K-means clustering Monte Carlo method Gauss–Hermite quadrature Statistics Applied mathematics Probability and statistics
897	Introduction to stream: An extensible Framework for Data Stream Clustering Research with R Michael Hahsler Matthew Bola˜ nos Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-06-16 18:19:30 Data analysis Multivariate statistics Cluster analysis Data stream clustering BIRCH K-means clustering DBSCAN Hierarchical clustering Weka Statistics Data mining Machine learning
898	A Discriminative Framework for Clustering via Similarity Functions Maria-Florina Balcan∗ Avrim Blum∗ Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2008-03-29 00:03:04 Geostatistics Machine learning Single-linkage clustering Hierarchical clustering K-medians clustering K-means clustering Correlation clustering Spectral clustering Consensus clustering Statistics Cluster analysis Data mining
899	ON DETERMINING THE NUMBER OF CLUSTERS–A COMPARATIVE STUDY By Brian Bies Kathryn Dabbs and Add to Reading List Source URL: ima.umn.edu Language: English - Date: 2011-10-06 10:33:24 Determining the number of clusters in a data set K-means clustering Spectral clustering Computer cluster Fuzzy clustering Consensus clustering Statistics Cluster analysis Multivariate statistics
900	Finding low error clusterings Maria-Florina Balcan Mark Braverman Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2009-05-21 20:12:17 Machine learning Data mining Geostatistics K-means clustering Correlation clustering K-medians clustering Data stream clustering Single-linkage clustering Statistics Cluster analysis Multivariate statistics

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