## Hard clustering with K-means

kmeans Theory¶ K-Means is the simplest and most fundemental clustering algorithm. Given: $x_1,x_2,…,x_n$, Where $x \in I\!R^d$ Output: Clusters $C_1,C_2,…,C_n$, Where $C_i \in \{1,2,..K\}$ Goal: Partition data into K clusters(groups) where each cluster has similar data. The goal is pretty clear. you have a bunch of data from which you may or may not know the generative distrubition. you want to learn the structure of the data in a such