- Referenced in 275 articles
- Algorithm AS 136: A K-Means Clustering Algorithm...
- Referenced in 406 articles
- systems, including randomized distributed algorithms, manufacturing systems and workstation clusters...
- Referenced in 605 articles
- fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging...
- Referenced in 105 articles
- Affinity propagation (AP) is a clustering algorithm that has been introduced by Brendan J. Frey ... follows: ”An algorithm that identifies exemplars among data points and forms clusters of data points ... until a good set of exemplars and clusters emerges.” AP has been applied in various ... important. Frey and Dueck have made their algorithm available as Matlab code. Matlab, however...
- Referenced in 257 articles
- Mixture Modeling fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including...
- Referenced in 106 articles
- Dueck’s Affinity Propagation clustering in R. The algorithms are largely analogous to the Matlab ... affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used...
- Referenced in 121 articles
- principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently...
- Referenced in 47 articles
- conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically...
- Referenced in 45 articles
- paper describes a novel graph theoretic clustering algorithm, ”Molecular Complex Detection” (MCODE), that detects densely ... given parameters. The algorithm has the advantage over other graph clustering methods of having ... network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction ... which correspond to known protein complexes. The algorithm is not affected by a known high...
- Referenced in 40 articles
- idea by describing an advanced multiobjective clustering algorithm, MOCK, with the capacity to identify good ... determine the number of clusters in a data set. The algorithm has been subject ... which explain its superiority to single-objective clustering techniques, and we analyse how MOCK ... single-objective algorithms run with a range of different numbers of clusters specified...
- Referenced in 46 articles
- method, called OSCAR (octagonal shrinkage and clustering algorithm for regression), is proposed to simultaneously select...
- Referenced in 269 articles
- SMPs and Cluster of SMPs. Automatic combination of iterative and direct solver algorithms to accelerate...
- Referenced in 31 articles
- Simulating Data to Study Performance of Clustering Algorithms. MixSim allows simulating mixtures of Gaussian distributions ... readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets ... systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of MixSim...
- Referenced in 50 articles
- analysis (multi-dimensional histogramming, fitting, minimization, cluster finding algorithms) and visualization tools. The user interacts...
- Referenced in 119 articles
- whether differences in the performance of two algorithms are significant or not; a normalised average ... positive instances; a clustering method for visualising the performance across multiple algorithms so that...
- Referenced in 119 articles
- squared distance between points in the same cluster. Although it offers no accuracy guarantees ... obtain an algorithm that is Θ(logk)-competitive with the optimal clustering. Preliminary experiments show...
- Referenced in 23 articles
- flexclust: Flexible Cluster Algorithms , The main function kcca implements a general framework for k-centroids...
- Referenced in 16 articles
- provides five biclustering and two standard clustering algorithms. BicAT works on Windows, Solaris, Linux ... interface for several existing biclustering and clustering algorithms. The main purpose of the tool...
- Referenced in 419 articles
- toolbox for reliable computing and self-validating algorithms. It comprises of self-validating methods ... univariate and multivariate nonlinear equations (simple and clusters) eigenvalue problems (simple and clusters, also inner...
- Referenced in 99 articles
- systems. A new constructive neural network learning algorithm (DistAl) based on inter-pattern distance ... Each neuron is designed to determine a cluster of training patterns belonging to the same ... significant advantage over other constructive learning algorithms that use an iterative (and often time consuming ... strategy to train individual neurons. The individual clusters (represented by the hidden neurons) are combined...