An in-Depth Review of Big Data Analytic Models for Clustering Operations

Trishali Dhote and Pragati Patil

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 5, 30-47

Abstract: The ever-increasing volume, pace, and variety of data in the present environment of data-driven decision-making need creative ways for extracting insightful information sets. A key component of data analysis, clustering techniques are essential for identifying latent patterns and structures in huge datasets. This work conducts a thorough investigation of big data analytic models for clustering operations in response to the pressing requirement to harness the power of big data analytics for effective and accurate clustering process. The necessity for this effort derives from the expanding levels of data volume and complexity that characterise modern information ecosystems. While effective in smaller datasets, conventional clustering approaches fall short when faced with the enormous datasets typical in contemporary applications. As a result, choosing and using the right big data analytic models for clustering have become crucial tasks for both researchers and practitioners. The review procedure used here is defined by a thorough and comprehensive approach. The first stage includes a thorough literature review in which a wide range of big data analytical models are methodically developed. These models cover a broad range of strategies, from hierarchical and model-based approaches to density-based and partitioning techniques. The foundation for the future research is laid by this thorough assessment, which focuses on a detailed analysis of the characteristics and performance measures of each model process. The empirical assessment considers a wide range of factors, including accuracy, computational complexity, scalability, and applicability for various application areas. A comprehensive knowledge of each model's potential and constraints is revealed by closely examining each model's performance across these aspects. This not only encourages a thorough understanding of the models' capabilities but also equips practitioners with the knowledge they need to carefully choose the best model for their unique clustering jobs.

Keywords: Big Data Analytics; Clustering Operations; Data Complexity; Analytic Models; Empirical Evaluation Process (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390455
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