Exploring Big Data Clustering: Approaches, Algorithms, and Platforms
Year: 2025
Type: article
Abstract: Clustering as the problem of discovering natural grouping in data has gotten a lot of attention due to its wide range of applications in health care, customer segmentation, image processing & transformation, market and recommendation systems, social network analysis, etc. It is an unsupervised learning task used to discover similar objects in a large dataset without relying on any prior information and gathering them into the same group. With the rapid growth of big data as result of data sets acquired by mobile devises, cameras, various sensors and other sources has necessitated research into extracting valuable information from enormous data sets. In this paper, we looked at different big data clustering approaches in the context of general clustering methods. In addition, we discussed several similarity measures as well as key clustering challenges such as cluster tendency assessment and cluster validity.
Source: CyberLeninK (CyberLeninka)
Authors: Aliguliyev, Ramiz, Badalov, Tural
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6888853529
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