Big Data Clustering Techniques Challenged and Perspectives: Review
Fouad H. Awad, Murtadha Mohammed Hamad · Informatica · 2023
Clustering in big data considers a critical data mining and analysis technique. There are problems with adapting clustering algorithms to large amounts of data, along with 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 overcome this issue and utilize clustering techniques for big data promptly. The purpose of this work is to investigate the history and advancement of clustering platforms and techniques to handle big data issues, from the basic 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 the main characteristics and drawbacks of each solution. In addition, an example of big data set clustering has been presented for a further overview of the clustering techniques.