Kmeans Clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. Kmeans clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult, however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectationmaximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, kmeans clustering tends to find clusters of comparable spatial extent, while the expectationmaximization mechanism allows clusters to have different shapes. The algorithm has a loose relationship to the knearest neighbor classifier, a popular machine learning technique for classification that is often confused with kmeans because of the k in the name. One can apply the 1nearest neighbor classifier on the cluster centers obtained by kmeans to classify new data into the existing clusters. This is known as nearest centroid classifier or Rocchio algorithm.
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