Clustering Method

Clustering methods aim to group similar data points together, revealing underlying structures and patterns within datasets. Current research emphasizes improving the scalability and efficiency of existing algorithms like k-means and k-medoids, particularly for high-dimensional and large datasets, with techniques such as whale optimization and granular-ball computing being explored. These advancements are crucial for various applications, including recommender systems, astronomy, financial crime detection, and even optimizing resource-constrained systems like embedded devices, by enabling efficient knowledge discovery from increasingly complex data.