期刊
IEEE ACCESS
卷 9, 期 -, 页码 80015-80027出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3084057
关键词
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
In the era of big data, traditional clustering algorithms face high computational costs, making it challenging to accurately process massive amounts of data in crucial moments. Despite the development of different algorithms to facilitate clustering processes, there are still many difficulties when dealing with large data volumes.
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.
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