Machine Learning, Artificial Intelligence, Technology, Python, Data Science.
to implement. - Converges to a solution in a finite number of steps. Weaknesses of K-Means Clustering: - Requires the number of clusters (K) to be predefined. - Sensitive to initial centroid positions, which affects the final results. - Non-ideal for clustering irregularly shaped or overlapping