Clustering Techniques: K-Means vs Hierarchical Clustering
Introduction: Clustering is an important aspect of data science used globally to identify concealed data patterns when no labels are available. Regardless of whether it is customer segmentation, image compression, or anomaly detection, clustering methods enable organizations to make sense of complex datasets without conditional outcomes. Clustering and, in particular, the distinction between K-Means and Hierarchical Clustering are part of the benchmarks that any individual who aims to obtain the best data science course in Bangalore must know. The two methods are the most common methods of teaching and application in a real-life data science project under the concept of clustering algorithms. It is a practical, no-expert-required but professional comparison of K-Means vs Hierarchical Clustering to help you grasp the functionality of each, their areas of strength and weakness, along with when to apply each. Introduction to Clustering in Data Science: Clustering is an unsupervised machi...