- Referenced in 7103 articles
- classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible...
- Referenced in 384 articles
- stand-alone Fortran programs for cluster analysis. The programs are described and illustrated ... PAM.FOR (partitions the data set into clusters with a new method using medoids); Chapter ... CLARA.FOR (for clustering large applications); Chapter 4: FANNY.FOR (a new method for fuzzy clustering); Chapter ... TWINS.FOR (hierarchical clustering; you can choose between agglomerative and divisive); Chapter 7: MONA.FOR (divisive hierachical...
- Referenced in 379 articles
- univariate and multivariate nonlinear equations (simple and clusters) eigenvalue problems (simple and clusters, also inner ... structured matrices) generalized eigenvalue problems (simple and clusters) quadrature for univariate functions univariate polynomial zeros ... simple and clusters) interval arithmetic for real and complex data including vectors and matrices (very...
- Referenced in 564 articles
- followed by a Hough transform to identify clusters belonging to a single object, and finally...
- Referenced in 388 articles
- Intel series, TM CM-5, clusters of workstations, and any system for which...
- Referenced in 387 articles
- randomized distributed algorithms, manufacturing systems and workstation clusters...
- Referenced in 377 articles
- modelling, statistical tests, time series analysis, classification, clustering, etc. Please consult the R project homepage...
- Referenced in 360 articles
- analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, and nonparametric analysis. A few examples...
- Referenced in 341 articles
- single PC or a cluster of the world’s largest supercomputers, the NAG Library...
- Referenced in 221 articles
- mclust: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation , Normal Mixture Modeling ... fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization...
- Referenced in 240 articles
- Algorithm AS 136: A K-Means Clustering Algorithm...
- Referenced in 233 articles
- with complete pivoting. Parallel on SMPs and Cluster of SMPs. Automatic combination of iterative...
Neural Network Toolbox
- Referenced in 166 articles
- applications such as data fitting, pattern recognition, clustering, time-series prediction, and dynamic system modeling ... data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox...
- Referenced in 92 articles
- apcluster: Affinity Propagation Clustering. The apcluster package implements Frey’s and Dueck’s Affinity Propagation ... clustering in R. The algorithms are largely analogous to the Matlab code published by Frey ... algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained ... Various plotting functions are available for analyzing clustering results...
- Referenced in 92 articles
- Affinity propagation (AP) is a clustering algorithm that has been introduced by Brendan J. Frey ... identifies exemplars among data points and forms clusters of data points around these exemplars ... until a good set of exemplars and clusters emerges.” AP has been applied in various ... implements leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis...
- Referenced in 163 articles
- distributed memory parallel computers including PC clusters...
- Referenced in 159 articles
- spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead...
- Referenced in 107 articles
- distributed processing of large data sets across clusters of computers using simple programming models ... highly-available service on top of a cluster of computers, each of which...
- Referenced in 142 articles
- suggested by polynomial algebra combined with a clustering principle based on elementary probability theory...
- Referenced in 87 articles
- means method is a widely used clustering technique that seeks to minimize the average squared ... distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity ... logk)-competitive with the optimal clustering. Preliminary experiments show that our augmentation improves both...