K-means clustering
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians and k-medoids.
Affinity propagationAlgorithms for k-means clusteringAngela Y. WuApache IgniteApache SparkApplications of k-means clusteringApproximate computingAutomatic clustering algorithmsBFR algorithmBIRCHBag-of-words model in computer visionBalanced clusteringBioinformaticsBiomedical text miningBiostatisticsBlock-matching and 3D filteringCURE algorithmCanopy clustering algorithmCentral tendencyCentroidCentroidal Voronoi tessellationChoropleth mapChristine PiatkoClustalCluster analysisCluster hypothesisCluster seekingColor Cell CompressionColor quantizationConsensus clusteringConstellation modelDBSCANData mining in agricultureData stream clusteringDavid MountDavies–Bouldin indexDetermining the number of clusters in a data setDirichlet processDocument clusteringEEG microstates
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K-means clustering
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians and k-medoids.
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Algorytm centroidów (k-średnic ...... Buzo i Graya – algorytmem LBG.
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Em mineração de dados, agrupam ...... permite ter diferentes formas.
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K-medias es un método de agrup ...... upos tengan formas diferentes.
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L'algorisme K-means és un mèto ...... utilitzat en mineria de dades.
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L'algoritmo K-means è un algor ...... formino uno spazio vettoriale.
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Le partitionnement en k-moyenn ...... e quantification de Lloyd-Max.
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Pengklasteran k rata-rata (bah ...... ara ini disebut sebagai atau .
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k-means (anglicky „k průměrů“) ...... ojde k ustálení (konvergence).
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k-means clustering is a method ...... assifier or Rocchio algorithm.
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k-均值算法(英文:k-means clustering)源 ...... 与k-近邻之间没有任何关系(后者是另一流行的机器学习技术)。
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Algorytm centroidów (k-średnic ...... Buzo i Graya – algorytmem LBG.
@pl
Em mineração de dados, agrupam ...... dos em um Diagrama de Voronoi.
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K-medias es un método de agrup ...... de datos en celdas de Voronoi.
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L'algorisme K-means és un mèto ...... utilitzat en mineria de dades.
@ca
L'algoritmo K-means è un algor ...... formino uno spazio vettoriale.
@it
Le partitionnement en k-moyenn ...... e des carrés de ces distances.
@fr
Pengklasteran k rata-rata (bah ...... n dapat mencapai dengan cepat.
@in
k-means (anglicky „k průměrů“) ...... se poloha centroidů neustálí.
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k-means clustering is a method ...... using k-medians and k-medoids.
@en
k-均值算法(英文:k-means clustering)源 ...... 与k-近邻之间没有任何关系(后者是另一流行的机器学习技术)。
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label
Algorisme k-means
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Algorytm centroidów
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K-Means-Algorithmus
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K-means clustering
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K-means
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K-means
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K-means
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K-medias
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K-moyennes
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K-平均算法
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