An Optimized Version of the K-Means Clustering Algorithm
Annals of Computer Science and Information Systems2014Vol. 2, pp. 695–699
Citations Over TimeTop 10% of 2014 papers
Abstract
This paper introduces an optimized version of the standard K-Means algorithm. The optimization refers to the running time and it comes from the observation that after a certain number of iterations, only a small part of the data elements change their cluster, so there is no need to re-distribute all data elements. Therefore the implementation proposed in this paper puts an edge between those data elements which won't change their cluster during the next iteration and those who might change it, reducing significantly the workload in case of very big data sets. The prototype implementation showed up to 70% reduction of the running time.
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