• AS 136

  • Referenced in 339 articles [sw14176]
  • Algorithm AS 136: A K-Means Clustering Algorithm...
  • k-means++

  • Referenced in 176 articles [sw21622]
  • squared distance between points in the same cluster. Although it offers no accuracy guarantees ... augmenting k-means with a very simple, randomized seeding technique, we obtain an algorithm that ... logk)-competitive with the optimal clustering. Preliminary experiments show that our augmentation improves both ... speed and the accuracy of k-means, often quite dramatically...
  • impute

  • Referenced in 104 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...
  • GAKREM

  • Referenced in 10 articles [sw02712]
  • introduce a novel clustering algorithm named GAKREM (Genetic Algorithm K-means Logarithmic Regression Expectation Maximization ... 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...
  • FGKA

  • Referenced in 6 articles [sw37722]
  • Genetic K-means Clustering Algorithm. In this paper, we propose a new clustering algorithm called ... Fast Genetic K-means Algorithm (FGKA). FGKA is inspired by the Genetic K-means Algorithm...
  • 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...
  • cclust

  • Referenced in 11 articles [sw11278]
  • K-means algorithm, On-line Update algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft ... several indexes for finding the number of clusters in a data...
  • fclust

  • Referenced in 9 articles [sw22644]
  • from the well-known fuzzy k-means (fkm) clustering algorithm, an increasing number of papers...
  • skmeans

  • Referenced in 4 articles [sw24387]
  • package skmeans: Spherical k-Means Clustering. Algorithms to compute spherical k-means partitions. Features several...
  • RSKC

  • Referenced in 3 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...
  • Vlfeat

  • Referenced in 46 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...
  • ParaKMeans

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

  • Referenced in 3 articles [sw02795]
  • This paper presents a subspace k-means clustering algorithm for high-dimensional data with automatic ... fuzzy k-means clustering process to enable several clusters to compete for objects, which leads ... some cluster centres and the identification of the `true’ number of clusters. The algorithm determines...
  • Statistics Toolbox

  • Referenced in 18 articles [sw10157]
  • learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-means ... hierarchical clustering, k-nearest neighbor search, Gaussian mixtures, and hidden Markov models...
  • GAPS

  • Referenced in 14 articles [sw02613]
  • distance based clustering algorithm is able to detect any type of clusters, irrespective of their ... based clustering technique SBKM, its modified version, and the well-known K-means algorithm. Sixteen...
  • DIVCLUS-T

  • Referenced in 5 articles [sw02736]
  • Ward agglomerative hierarchical clustering algorithm and the k-means partitioning algorithm, it is based ... k-means, it provides a simple and natural interpretation of the clusters. The price paid ... interpretation is studied by applying the three algorithms on six databases from the UCI Machine...
  • StreamKM++

  • Referenced in 14 articles [sw25552]
  • weighted k-means algorithm is applied on the coreset to get the final clusters...
  • clustMixType

  • Referenced in 3 articles [sw16328]
  • Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical...
  • flowPeaks

  • Referenced in 4 articles [sw38205]
  • clustering for flow cytometry data via K-means and density peak finding. Motivation: For flow ... common approaches to the unsupervised clustering problem: one is based on the finite mixture model ... computationally slow and has difficulty to identify clusters of irregular shapes. The latter approach cannot ... identify irregular shape clusters. The algorithm first uses K-means algorithm with a large...
  • Canopy

  • Referenced in 1 article [sw40794]
  • usage control based on Canopy_K-means clustering and WARM. Medical consumable usage is ineluctable ... usage control method based on Canopy_K-means and Weighted Association Rules Mining (WARM ... proposed in this paper. Firstly, Canopy algorithm ... used to get rough clusters; Secondly, K-means algorithm is used to get accurate clusters...