Scalable Clustering Algorithms for Big Data: A Review

作     者：Mahdi, Mahmoud A. Hosny, Khalid M. Elhenawy, Ibrahim 

作者机构：Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2021年第9卷

页      面：80015-80027页

核心收录：

基　　金：Fondazione Telethon Ministero dell’Istruzione, dell’Università e della Ricerca Associazione Italiana per la Ricerca sul Cancro 

主　　题：Clustering algorithms Big Data Scalability Partitioning algorithms Data mining Licenses Classification algorithms Clustering unsupervised learning traditional clustering parallel clustering stream clustering high dimensional data big data large-scale 

摘      要：Clustering algorithms have become one of the most critical research areas in multiple domains, especially data mining. However, with the massive growth of big data applications in the cloud world, these applications face many challenges and difficulties. Since Big Data refers to an enormous amount of data, most traditional clustering algorithms come with high computational costs. Hence, the research question is how to handle this volume of data and get accurate results at a critical time. Despite ongoing research work to develop different algorithms to facilitate complex clustering processes, there are still many difficulties that arise while dealing with a large volume of data. In this paper, we review the most relevant clustering algorithms in a categorized manner, provide a comparison of clustering methods for large-scale data and explain the overall challenges based on clustering type. The key idea of the paper is to highlight the main advantages and disadvantages of clustering algorithms for dealing with big data in a scalable approach behind the different other features.