Big Data Clustering Techniques Challenged and Perspectives: Review

Abstract

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.Povzetek: Članek predstavlja pregled tehnik gručenja za velike podatke.

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

“…Notably, using only the WoS database is considered sufficient for retrieving clustering-related articles. Additionally, many review papers rely on the official website [17][18][19]24] or the publisher's website [20][21][22] as their primary source. In another study, Wang et al [23] retrieved articles indexed by Google Scholar and the WoS database.…”

“…Furthermore, various other taxonomies and surveys exist, covering topics that focus on determining the cluster number [19], machine-learning-based clustering [20], big data clustering [21,22], density peak clustering [23], subspace clustering for high-dimensional data [24], automatic clustering [25], and how nature-inspired metaheuristic techniques are implemented into automatic clustering [26].…”

Improving Task Scheduling In Cloud Datacenters By Implementation Of An Intelligent Scheduling Algorithm

“…In this case, CA will manage Task Scheduling by utilizing a cost function that aims to minimize both the make span and the overall time of the tasks. The make span is determined through the following equations [24,27]:…”