Open AccessEfficient algorithm for big data clustering on single machine
Author(s) -
Alguliyev Rasim M.,
Aliguliyev Ramiz M.,
Sukhostat Lyudmila V.
Publication year - 2020
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2019.0048
Subject(s) - cluster analysis , centroid , computer science , data mining , computation , big data , cure data clustering algorithm , algorithm , cluster (spacecraft) , canopy clustering algorithm , correlation clustering , artificial intelligence , programming language
Big data analysis requires the presence of large computing powers, which is not always feasible. And so, it became necessary to develop new clustering algorithms capable of such data processing. This study proposes a new parallel clustering algorithm based on the k‐means algorithm. It significantly reduces the exponential growth of computations. The proposed algorithm splits a dataset into batches while preserving the characteristics of the initial dataset and increasing the clustering speed. The idea is to define cluster centroids, which are also clustered, for each batch. According to the obtained centroids, the data points belong to the cluster with the nearest centroid. Real large datasets are used to conduct the experiments to evaluate the effectiveness of the proposed approach. The proposed approach is compared with k‐means and its modification. The experiments show that the proposed algorithm is a promising tool for clustering large datasets in comparison with the k‐means algorithm.