Efficient algorithm for big data clustering on single machine

• 作者: Lyudmila V.Sukhostat Ramiz M.Aliguliyev Rasim M.Alguliyev
• 来源期刊: 智能技术学报
• 学科: 工学
• 关键词: algorithm. ALGORITHM BATCH
• 年，卷（期）: 2020,（1）
• 页码范围: 9-14
• 页数: 6页
• 分类号: TP3

摘要

Big data analysis requires the presence of large computing powers, which is not always feasible. And so, itbecame necessary to develop new clustering algorithms capable of such data processing. This study proposes a newparallel clustering algorithm based on the k-means algorithm. It significantly reduces the exponential growth ofcomputations. The proposed algorithm splits a dataset into batches while preserving the characteristics of the initialdataset and increasing the clustering speed. The idea is to define cluster centroids, which are also clustered, for eachbatch. According to the obtained centroids, the data points belong to the cluster with the nearest centroid. Real largedatasets are used to conduct the experiments to evaluate the effectiveness of the proposed approach. The proposedapproach is compared with k-means and its modification. The experiments show that the proposed algorithm is apromising tool for clustering large datasets in comparison with the k-means algorithm.