Published Paper Details:

Clustering, a fundamental technique in machine learning, plays a pivotal role in pattern recognition, data mining, and exploratory data analysis. This paper provides a comprehensive exploration of clustering algorithms, evaluation metrics, applications, challenges, and recent advancements in the field. We discuss various types of clustering algorithms including partitioning-based, density-based, hierarchical, and distribution-based methods, along with their strengths and limitations. Evaluation metrics for assessing the quality of clusters are examined, encompassing both internal and external measures. Real-world applications of clustering across diverse domains such as image segmentation, customer segmentation, anomaly detection, and document clustering are elucidated. Furthermore, we delve into the challenges faced by clustering algorithms including sensitivity to initialization, scalability issues, and interpretability concerns.