Lloyd's K-means  Lloyd's K-means: a simple and popular method for clustering – Implicitly assumes data points are in Euclidean space  K-means finds
{"Image DocumentName": "Lloyd\u2019s K-meansLloyd\u2019s K-means: a simple and popular method for clustering. Implicitly assumes data points are in Euclidean space.", "Image ImageDescription": "K-means finds a local minimum of the objective function. Objective: (average) sum of squared distances from cluster centre. Effectively, aims to minimize the intra-cluster variance. Initialize by picking k points randomly from the data. Repeatedly alternate two phases: Assign each input point to its closest centre. Compute centroid of each cluster (average point) Replace cluster centers with centroids. Until process converges / hit a limit on number of iterations.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}