Add to ORKGThe k-means algorithm is a widely used clustering tech-nique. Here we will examine the performance of multiple implementations of the k-means algorithm in different set-tings. Our discussion will touch on the implementation of the algorithm in both python and C, and will also mention a 3rd party package for the k-means algorithm that is also written in C but provides python bindings. We will then turn to focus on the parallelization of the k-means algorithm and discuss both implementation and performance. 1
He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrat...
The K-Means algorithm is one the most efficient and widely used algorithms for clustering data. Howe...
<p>Performance of standard k-means, sparse k-means and randomized sparse k-means clustering algorith...
Abstract: K-means algorithm is a popular, unsupervised and iterative clustering algorithmwell known ...
This research primarily focused on finding differences in various distancing methods used in the k-m...
International audiencek-means is a standard algorithm for clustering data. It constitutes generally ...
In data mining, cluster analysis is one of challenging field of research. Cluster analysis is called...
4th IEEE International Congress on Big Data, BigData Congress ( 2015 : New York City; United States...
G-means is a data mining clustering algorithm based on k-means, used to find the number of Gaussian ...
We present polynomial upper and lower bounds on the number of iterations performed by the k-means me...
<p>This constitute simulation of three large data sets in the order of; 10,000×50, 30,000×50 and 50,...
<p>Performance of standard K-means, sparse K-means and randomized K-mean clustering algorithm using ...
Parallel efficiency comparison in the algorithm in this paper and DP K-means.</p
The K-means algorithm is one of the most popular algorithms in Data Science, and it is aimed to disc...
(A) The clustering runtime vs. the number of cells in the simulated datasets for all four methods. (...
He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrat...
The K-Means algorithm is one the most efficient and widely used algorithms for clustering data. Howe...
<p>Performance of standard k-means, sparse k-means and randomized sparse k-means clustering algorith...
Abstract: K-means algorithm is a popular, unsupervised and iterative clustering algorithmwell known ...
This research primarily focused on finding differences in various distancing methods used in the k-m...
International audiencek-means is a standard algorithm for clustering data. It constitutes generally ...
In data mining, cluster analysis is one of challenging field of research. Cluster analysis is called...
4th IEEE International Congress on Big Data, BigData Congress ( 2015 : New York City; United States...
G-means is a data mining clustering algorithm based on k-means, used to find the number of Gaussian ...
We present polynomial upper and lower bounds on the number of iterations performed by the k-means me...
<p>This constitute simulation of three large data sets in the order of; 10,000×50, 30,000×50 and 50,...
<p>Performance of standard K-means, sparse K-means and randomized K-mean clustering algorithm using ...
Parallel efficiency comparison in the algorithm in this paper and DP K-means.</p
The K-means algorithm is one of the most popular algorithms in Data Science, and it is aimed to disc...
(A) The clustering runtime vs. the number of cells in the simulated datasets for all four methods. (...
He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrat...
The K-Means algorithm is one the most efficient and widely used algorithms for clustering data. Howe...
<p>Performance of standard k-means, sparse k-means and randomized sparse k-means clustering algorith...