In K-means clustering, the partitioning is known as a "hard partition," meaning each point is strictly assigned to a single cluster. This method does not allow for probability distributions across clusters as in the Gaussian Mixture model. As a result, K-means may struggle with data sets where clusters overlap significantly, as a straight boundary tends to be drawn between them. This limitation can lead to less defined clusters, especially when the clusters are not well-separated in space. This behavior is evident in the plot shown in Figure , where cluster two is distinctly different, but clusters zero and one could potentially be merged into a single cluster. This ambiguity suggests that using a different clustering algorithm, such as hierarchical clustering, might yield different results, potentially combining these clusters into one. However, with K-means, the data set will consistently be partitioned into distinct parts, irrespective of such nuances.