Machine Learning, Artificial Intelligence, Technology, Python, Data Science.
Can be challenging to select the appropriate number of clusters from the dendrogram. - Prone to producing imbalanced clusters when dealing with unevenly distributed data. - Sensitive to noise and outliers, which can affect the clustering hierarchy. In summary, while K-Means clustering is efficient