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Fuzzy clustering
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161	Package ‘vegclust’ July 2, 2014 Type Package Title Fuzzy clustering of vegetation data Version[removed]Date[removed] Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-07-02 16:48:46 Cluster analysis Data analysis Data mining Fuzzy clustering K-means clustering Medoid Statistical classification Matrix Wetland Statistics Machine learning Multivariate statistics
162	How to use the vegclust package (ver[removed]Miquel De C´aceres1 1 Centre Tecnol`ogic Forestal de Catalunya. Ctra. St. Lloren¸c de Morunys km 2, 25280, Solsona, Catalonia, Spain Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-07-02 16:48:46 K-means clustering K-medoids Fuzzy clustering Statistics Cluster analysis Medoid
163	Microsoft Word - APR[removed]docx Add to Reading List Source URL: www.atmospolres.com Language: English - Date: 2014-09-27 08:48:49 Multivariate statistics Fuzzy clustering Marmara Region Principal component analysis Chemometrics Air pollution Bursa Sea of Marmara Statistics Science Data analysis
164	AMERICAN METEOROLOGICAL SOCIETY Journal of Climate EARLY ONLINE RELEASE Add to Reading List Source URL: www.soest.hawaii.edu Language: English - Date: 2013-10-29 16:26:25 Fuzzy clustering Cluster analysis K-means clustering Hierarchical clustering Dunn index Consensus clustering Statistics Multivariate statistics Geostatistics
165	This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and shar Add to Reading List Source URL: www.afpmb.org Language: English - Date: 2011-12-01 02:23:03 Cybernetics Neuro-fuzzy Intelligent control Fuzzy logic Fuzzy control system Machine learning Soft computing Neural network Fuzzy clustering Artificial intelligence Logic Science
166	Supervised Learning in Parallel Universes Using Neighborgrams Bernd Wiswedel and Michael R. Berthold Department of Computer and Information Science, University of Konstanz, Germany Abstract. We present a supervised meth Add to Reading List Source URL: www.inf.uni-konstanz.de Language: English - Date: 2013-11-18 09:18:15 Data mining Geostatistics Machine learning Multiverse Consensus clustering Fuzzy clustering Statistics Cluster analysis Data analysis
167	doi:[removed]j.ijar[removed] Add to Reading List Source URL: www.inf.uni-konstanz.de Language: English - Date: 2010-11-08 10:11:58 Fuzzy clustering K-means clustering DBSCAN K-medoids Multiverse Consensus clustering Clustering high-dimensional data Statistics Cluster analysis Multivariate statistics
168	Fuzzy Clustering in Parallel Universes with Noise Detection Bernd Wiswedel and Michael R. Berthold Department of Computer and Information Science, University of Konstanz[removed]Konstanz, Germany {wiswedel,berthold}@inf.un Add to Reading List Source URL: www.inf.uni-konstanz.de Language: English - Date: 2010-11-08 10:11:58 Machine learning Cluster analysis K-means clustering K-medoids DBSCAN Consensus clustering CURE data clustering algorithm Statistics Fuzzy clustering Multivariate statistics
169	Mining of Cell Assay Images Using Active Semi-Supervised Clustering ∗ Nicolas Cebron and Michael R. Berthold ALTANA Chair for Bioinformatics and Information Mining Department of Computer and Information Science Konstan Add to Reading List Source URL: www.inf.uni-konstanz.de Language: English - Date: 2010-11-08 10:09:25 Geostatistics Multivariate statistics Data mining Fuzzy clustering Segmentation Categorization Unsupervised learning Pattern recognition Constrained clustering Statistics Machine learning Cluster analysis
170	Solution Adapted Nested Grid Refinement for 2-D PDEs James. M. Hyman1 and Shengtai Li1,2 1 Theoretical Division, Mail Stop, B284 Add to Reading List Source URL: math.lanl.gov Language: English - Date: 2004-10-22 11:20:34 Data mining Geostatistics Machine learning Adaptive mesh refinement Single-linkage clustering MEMO Model Fuzzy clustering Statistics Cluster analysis Data analysis

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