• AS 136

  • Referenced in 251 articles [sw14176]
  • Algorithm AS 136: A K-Means Clustering Algorithm...
  • SuLQ

  • Referenced in 120 articles [sw11355]
  • primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently...
  • k-means++

  • Referenced in 104 articles [sw21622]
  • augmenting k-means with a very simple, randomized seeding technique, we obtain an algorithm that ... speed and the accuracy of k-means, often quite dramatically...
  • ARPACK

  • Referenced in 774 articles [sw04218]
  • sparse or structured matrices A where structured means that a matrix-vector product ... operations. This software is based upon an algorithmic variant of the Arnoldi process called ... software is designed to compute a few (k) eigenvalues with user specified features such...
  • GAKREM

  • Referenced in 10 articles [sw02712]
  • novel clustering algorithm named GAKREM (Genetic Algorithm K-means Logarithmic Regression Expectation Maximization) that combines ... best characteristics of the K-means and EM algorithms but avoids their weaknesses such ... K-means to initially assign data points to clusters. The algorithm is evaluated by comparing ... with the conventional EM algorithm, the K-means algorithm, and the likelihood cross-validation technique...
  • GAPS

  • Referenced in 13 articles [sw02613]
  • with Point Symmetry (GAPS) distance based clustering algorithm is able to detect any type ... modified version, and the well-known K-means algorithm. Sixteen data sets with widely varying...
  • StreamKM++

  • Referenced in 11 articles [sw25552]
  • from the data stream, a weighted k-means algorithm is applied on the coreset...
  • impute

  • Referenced in 91 articles [sw14376]
  • with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete ... methods such as hierarchical clustering and K-means clustering are not robust to missing data ... range of data sets to which these algorithms can be applied. In this report ... Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average...
  • cclust

  • Referenced in 9 articles [sw11278]
  • Clustering Indexes. Convex Clustering methods, including K-means algorithm, On-line Update algorithm (Hard Competitive...
  • ParaKMeans

  • Referenced in 4 articles [sw29731]
  • ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use. Background: During ... perform cluster analysis. While many clustering algorithms have been developed, they all suffer a significant ... problem of clustering algorithms is to distribute or parallelize the algorithm across multiple computers. Results ... parallelized version of the K-means Clustering algorithm. Most parallel processing applications are not accessible...
  • DIVCLUS-T

  • Referenced in 5 articles [sw02736]
  • hierarchical clustering algorithm and the k-means partitioning algorithm, it is based on the minimization ... inertia criterion. However, unlike Ward and k-means, it provides a simple and natural interpretation ... interpretation is studied by applying the three algorithms on six databases from the UCI Machine...
  • T-REKS

  • Referenced in 3 articles [sw22483]
  • REpeats in sequences with a K-meanS based algorithm. MOTIVATION: Over the last years ... computer programs which were based on different algorithms have been developed. Nevertheless, our tests showed ... identical short strings by using a K-means algorithm. Benchmark of the existing programs...
  • BoostCluster

  • Referenced in 5 articles [sw08555]
  • number of popular clustering algorithms (K-means, partitional SingleLink, spectral clustering), and its performance ... comparable to the state-of-the-art algorithms for data clustering with side information...
  • pyMEF

  • Referenced in 2 articles [sw07455]
  • functions. They can be estimated by various means, often using expectation-maximization or kernel density ... known algorithms, new and promising stochastic modeling methods include Dirichlet process mixtures and $k$-maximum ... means-like algorithms. Like all $k$-means algorithms, our method relies on divergences and centroids...
  • skmeans

  • Referenced in 3 articles [sw24387]
  • package skmeans: Spherical k-Means Clustering. Algorithms to compute spherical k-means partitions. Features several...
  • Vlfeat

  • Referenced in 38 articles [sw13478]
  • open and portable library of computer vision algorithms. It aims at facilitating fast prototyping ... feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization...
  • Statistics Toolbox

  • Referenced in 18 articles [sw10157]
  • learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-means...
  • SMART

  • Referenced in 3 articles [sw02795]
  • This paper presents a subspace k-means clustering algorithm for high-dimensional data with automatic ... objective function of the fuzzy k-means clustering process to enable several clusters to compete ... true’ number of clusters. The algorithm determines the number of clusters in a dataset...
  • AntClust

  • Referenced in 10 articles [sw01754]
  • Gestalt” colonial odor. Similarly, our clustering algorithm associates an object of the data ... AntClust to the K-Means method and to the AntClass algorithm. We present new results...
  • RSKC

  • Referenced in 2 articles [sw23852]
  • Robust and Sparse K-Means Clustering Algorithm. Witten and Tibshirani (2010) proposed an algorithim ... select clustering variables, called sparse K-means (SK-means). SK-means is particularly useful when ... robust and sparse K-means clustering algorithm implemented in the R package RSKC. We demonstrate...