Open AccessParallel implementation of the greedy heuristic clustering algorithms
Author(s) -
Lev Kazakovtsev,
Ivan Rozhnov,
E. O. Popov,
М. В. Карасева,
Alena Stupina
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/537/2/022052
Subject(s) - greedy algorithm , heuristic , computer science , cluster analysis , greedy randomized adaptive search procedure , null move heuristic , algorithm , parallel computing , scale (ratio) , artificial intelligence , physics , quantum mechanics
Authors propose parallel greedy heuristic k-means clustering algorithms for implementation on the graphical processing units (GPU) for solving large-scale problems. The computational experiments illustrate high performance of the GPUs in comparison with running the greedy heuristic algorithms on a central processor unit which is especially significant in the case of big datasets and bug numbers of clusters. The efficiency of the greedy heuristic algorithms in comparison with the standard k-means algorithm remains.