K-means clustering - PDFSEARCH.IO - Document Search Engine

K-means clustering
Results: 985

#	Item
11	¨ ETH Zurich Computer Science Department Prof. Marc Pollefeys Prof. Luc Van Gool Add to Reading List Source URL: www.cvg.ethz.ch Language: English - Date: 2015-12-15 08:50:27 Artificial intelligence Statistics Machine learning Nonparametric statistics Statistical classification Feature detection Learning Histogram Naive Bayes classifier Outline of object recognition K-means clustering Bag-of-words model
12	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
13	Scalable Training of Mixture Models via Coresets Dan Feldman MIT Matthew Faulkner Add to Reading List Source URL: eew.caltech.edu Language: English - Date: 2012-05-15 11:47:37 Statistics Probability Maximum likelihood estimation Probability distributions Bayesian statistics Expectationmaximization algorithm Mixture model Normal distribution K-means clustering Likelihood function Data stream clustering Gamma distribution
14	Microsoft Word - articuloPoster2 Add to Reading List Source URL: columbus.exp.sis.pitt.edu Language: English - Date: 2014-09-24 15:13:20 Computing Statistics Learning Cluster analysis Data mining Geostatistics Multivariate statistics Unsupervised learning Computer cluster K-means clustering Teacher Data analysis
15	WORKING PAPERA NEW CLASSIFICATION OF UK LOCAL AUTHORITIES USING 2001 CENSUS KEY STATISTICS Add to Reading List Source URL: sasi.group.shef.ac.uk Language: English - Date: 2012-08-31 04:04:22 Statistics Mathematics Academia Machine learning Statistical classification K-means clustering Variable Level of measurement Determining the number of clusters in a data set Geodemographic segmentation Cluster analysis
16	Fast Hierarchical Clustering and Other Applications of Dynamic Closest Pairs David Eppstein∗ Abstract dynamic closest pair problem. It can be solved by brute force 2 We develop data structures for dynamic closest pair Add to Reading List Source URL: bioinfo.ict.ac.cn Language: English - Date: 2014-11-28 11:05:21 Mathematics Statistics Applied mathematics Data mining Search algorithms Approximation algorithms Discrete geometry Nearest neighbor search Numerical analysis Hierarchical clustering Cluster analysis K-means clustering
17	Comparison of Clustering Algorithms in the Context of Software Evolution Jingwei Wu, Ahmed E. Hassan, Richard C. Holt School of Computer Science University of Waterloo Waterloo ON, Canada j25wu,aeehassa,holt@uwaterloo.c Add to Reading List Source URL: plg.uwaterloo.ca Language: English - Date: 2005-07-08 17:27:32 Statistics Bioinformatics Cluster analysis Data mining Geostatistics Hierarchical clustering Sequence clustering Computational statistics Information Consensus clustering K-means++
18	81 Croatian Operational Research Review CRORR), 81–96 Research project grouping and ranking by using adaptive Add to Reading List Source URL: www.mathos.unios.hr Language: English - Date: 2016-05-12 02:29:38 Statistics Data analysis Computational statistics Cluster analysis Data mining Geostatistics K-means clustering Mahalanobis distance Hierarchical clustering
19	Multiple circle detection based on center-based clustering Rudolf Scitovskia , Tomislav Maroˇsevi´ca,∗ a Department of Mathematics, University of Osijek, Trg Lj. Gaja 6, HR – Osijek, Croatia Add to Reading List Source URL: www.mathos.unios.hr Language: English - Date: 2014-09-21 14:43:22 Mathematics Mathematical analysis Analysis Data mining Operations research K-means clustering Cluster analysis Fuzzy clustering Pi Partition Mathematical optimization
20	A Bayesian Approach to Learning 3D Representations of Dynamic Environments Ralf K¨astner, Nikolas Engelhard, Rudolph Triebel, and Roland Siegwart Abstract We propose a novel probabilistic approach to learning spatial re Add to Reading List Source URL: europa.informatik.uni-freiburg.de Language: English - Date: 2010-12-20 07:34:33 Statistics Statistical theory Estimation theory Bayesian statistics Statistical models Signal processing Cluster analysis Mixture model Maximum likelihood estimation Normal distribution K-means clustering Likelihood function

UPDATE