Data Science, Machine Learning, Unsupervised Learning.
right number of clusters for optimal results can be challenging and time-consuming. The k-means algorithm is sensitive to noise and outliers in the dataset. Outliers can greatly influence the clustering results and can distort the clustering results. The k-means algorithm is not suitable for