Data Clustering: Beyond K-Means in the Past 50 Years

Data clustering has been an important task in data mining for many years. K-means, as a classical algorithm, has been widely used for clustering analysis. However, with the development of data mining techniques, more advanced clustering algorithms have been proposed in the past 50 years. In this article, we discuss the developments and advancements of data clustering techniques beyond k-means. We introduce some state-of-the-art clustering algorithms, such as DBSCAN and OPTICS, and provide examples of their applications. Additionally, we compare the advantages and limitations of different clustering methods.