Accelerated K-means Clustering Algorithm
Автор: Preeti Jain, Bala Buksh
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 10 Vol. 8, 2016 года.
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Optimizing K-means is still an active area of research for purpose of clustering. Recent developments in Cloud Computing have resulted in emergence of Big Data Analytics. There is a fresh need of simple, fast yet accurate algorithm for clustering huge amount of data. This paper proposes optimization of K-means through reduction of the points which are considered for re-clustering in each iteration. The work is generalization of earlier work by Poteras et al who proposed this idea. The suggested scheme has an improved average runtime. The cost per iteration reduces as number of iterations grow which makes the proposal very scalable.
Clustering, k-means, Optimization of k-means, Distance metrics, Poteras et al's Scheme
Короткий адрес: https://sciup.org/15012564
IDR: 15012564
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