A Review of Approaches for Rapid Data Clustering: Challenges, Opportunities, and Future Directions

• Published in: IEEE access 2024, Vol.12, p.138086-138120
• Main Authors: Mahnoor, Shafi, Imran, Chaudhry, Mahnoor, Caro Montero, Elizabeth, Silva Alvarado, Eduardo, de la Torre Diez, Isabel, Abdus Samad, Md, Ashraf, Imran
• Format: Article
• Language: English
• Subjects: Algorithms; Anomalies; Clustering; Clustering algorithms; clustering applications; Clustering methods; Clustering technique; Data analysis; Data mining; hierarchical partition; Image segmentation; Knowledge management; Machine learning; Measurement; Outliers (statistics); Pattern analysis; Technological innovation; Unsupervised learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3461798

For organizing and analyzing massive amounts of data and revealing hidden patterns and structures, clustering is a crucial approach. This paper examines unique strategies for rapid clustering, highlighting the problems and possibilities in this area. The paper includes a brief introduction to clustering, discussing various clustering algorithms, improvements in handling various data types, and appropriate evaluation metrics. It then highlights the unsupervised nature of clustering and emphasizes its importance in many different fields, including customer segmentation, market research, and anomaly detection. This review emphasizes ongoing efforts to address these issues through research and suggests exciting directions for future investigations. By examining the advancements, challenges, and future opportunities in clustering, this research aims to increase awareness of cutting-edge approaches and encourage additional innovations in this essential field of data analysis and pattern identification. It highlights the need for resilience to noise and outliers, domain knowledge integration, scalable and efficient algorithms, and interpretable clustering technologies. In addition to managing high-dimensional data, creating incremental and online clustering techniques, and investigating deep learning-based algorithms, the study suggests future research areas. Additionally featured are real-world applications from several sectors. Although clustering approaches have made a substantial contribution, more research is necessary to solve their limitations and fully realize their promise for data analysis.