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Biblioqrafik təsvir | Alguliyev , R.M. Batch Clustering Algorithm for Big Data Sets / R.M. Alguliyev , R.M. Aliguliyev // 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT). - Bakı, 2016. - P. 79-82. | Annotasiya | Vast spread of computing technologies has led to
abundance of large data sets. Today tech companies like, Google,
Facebook, Twitter and Amazon handle big data sets and log
terabytes, if not petabytes, of data per day. Thus, there is a need
to find similarities and define groupings among the elements of
these big data sets. One of the ways to find these similarities is
data clustering. Currently, there exist several data clustering
algorithms which differ by their application area and efficiency.
Increase in computational power and algorithmic improvements
have reduced the time for clustering of big data sets. But it
usually happens that big data sets can’t be processed whole due
to hardware and computational restrictions. In this paper, the
classic k-means clustering algorithm is compared to the proposed
batch clustering (BC) algorithm for the required computation
time and objective function. The BC algorithm is designed to
cluster large data sets in batches but maintain the efficiency and
quality. Several experiments confirm that batch clustering
algorithm for big data sets is more efficient in using
computational power, data storage and results in better
clustering compared to k-means algorithm. The experiments are
conducted with the data set of 2 (two) million two-dimensional
data points. | | Elektron variant | Elektron variant |
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