k-均值聚类是一种矢量量化，最初从信号处理，这是流行的数据挖掘中的聚类分析的方法。k-均值聚类分区 n 个观测到 k 集群每个观察值属于最近均值集群目标，作为该群集的一个原型。这会导致 Voronoi 单元格数据空间的划分。问题是计算困难 （np） ；然而，有高效的启发式算法，并普遍采用和快速收敛到局部最优解。这些是通常类似于混合物通过这两种算法的迭代加细方法的高斯分布的期望最大化算法。此外，它们都使用聚类中心来模型数据 ；然而，k-均值聚类倾向找到集群的可比性的空间范围，而期望最大化机制允许群集，有不同的形状。
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