Big Data Clustering Techniques Challenges and Perspectives: Review

被引：0
作者：
Awad F.H. [1 ]
Hamad M.M. [1 ]
机构：
[1] College of Computer Science and Information Technology, University of Anbar, Anbar
来源：
Informatica (Slovenia) | 2023年 / 47卷 / 06期
关键词：
Big data; clustering; data mining; machine learning;
D O I：
10.31449/inf.v47i6.4445
中图分类号：
学科分类号：
摘要：
Clustering in big data is considered a critical data mining and analysis technique. There are issues with adapting clustering algorithms to large amounts of data and new challenges brought by big data. As the size of big data is up to petabytes of data, and clustering methods have high processing costs, the challenge is how to handle this issue and utilize clustering techniques for big data efficiently. This study aims to investigate the recent advancement of clustering platforms and techniques to handle big data issues, from the early suggested techniques to today's novel solutions. The methodology and specific issues for building an effective clustering mechanism are presented and evaluated, followed by a discussion of the choices for enhancing clustering algorithms. A brief literature review of the recent advancement in clustering techniques has been presented to address each solution's main characteristics and drawbacks. © 2023 Slovene Society Informatika. All rights reserved.
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收藏
页码：203 / 218
页数：15
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