Comprehensive Analysis of Clustering Methods: Focusing on Fuzzy Clustering
Vaishali P. Salve, Magan P. Ghatule · 2025
The objective of clustering algorithms is to group or cluster data according to the underlying patterns and commonalities in the data. They are crucial to many aspects of modern life, including social media, healthcare, marketing and e-commerce, and data organization and analysis. There are many clustering methods available, and new ones are always being developed. There are strengths and limitations associated with each algorithm, and there is currently no one method that works for every task. In this study, the researchers analyzed existing clustering algorithms and categorized popular algorithms along five dimensions: fundamental various clustering techniques, fuzzy clustering, the proposed Fuzzy C-Mean (FCM) clustering, algorithms are classified based on fundamental clustering method and data size, and it also offers a comparison of studies comparing Fuzzy C-Means (FCM) clustering with other traditional clustering algorithms. This classification makes it easier for researchers to understand clustering algorithms from different angles and helps in the identification of algorithms that are appropriate for resolving particular problems. Lastly, they discussed the current condition of clustering algorithms now and possible future developments. The researchers additionally discussed and noted the field's open difficulties and unresolved issues.