Efficient algorithm for big data clustering on single machine

CAAI Transactions on Intelligence Technology

article

Rasim M. Alguliyev¹  Ramiz M. Aliguliyev¹  Lyudmila V. Sukhostat¹ 

[1] Institute of Information Technology, Azerbaijan National Academy of Sciences

关键词: pattern clustering;    data analysis;    Big Data;    big data clustering;    single machine;    big data analysis;    computing powers;    clustering algorithms;    data processing;    k-means algorithm;    initial dataset;    clustering speed;    cluster centroids;    data points;    C6130 Data handling techniques;   

DOI  :  10.1049/trit.2019.0048

学科分类：数学（综合）

来源: Wiley

【 摘 要 】

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.

【 授权许可】

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