Machine Learning, Education, Scikit Learn, Unsupervised Learning, Clustering.
This means that the optimal number of clusters is 3. Issues Inherently, K-means is NP-hard. For d dimensions, k clusters, and n observations, we will find a solution in O(n^(dk+1)) in time. There are some heuristics to deal with this; an example is MiniBatch K-means, which takes portions (batches)