Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)

03/15/2019

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The initial centroid is a fairly challenging problem in the k-means method because it can affect the clustering results. In addition, choosing the starting centroid of the cluster is not always appropriate, especially, when the number of groups increases.

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Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)

Improved Performance of Unsupervised Method by Renovated K-Means

An enhanced method of initial cluster center selection for K-means algorithm

Generalized Dirichlet-process-means for f-separable distortion measures

An efficient K-means algorithm for Massive Data

Parallelization of Kmeans++ using CUDA

CKmeans and FCKmeans : Two Deterministic Initialization Procedures For Kmeans Algorithm Using Crowding Distance

Recombinator-k-means: Enhancing k-means++ by seeding from pools of previous runs

Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)

Related Research

Improved Performance of Unsupervised Method by Renovated K-Means

An enhanced method of initial cluster center selection for K-means algorithm

Generalized Dirichlet-process-means for f-separable distortion measures

An efficient K-means algorithm for Massive Data

Parallelization of Kmeans++ using CUDA

CKmeans and FCKmeans : Two Deterministic Initialization Procedures For Kmeans Algorithm Using Crowding Distance

Recombinator-k-means: Enhancing k-means++ by seeding from pools of previous runs