Chapter 9
k-Means Clustering
9.1 Introduction
The method of k-means clustering is one of vector quantization, originally from signal processing, which is popular for cluster analysis in data mining. This method of k-means 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 (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative ...
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