Non-Hierarchical Clustering
Non-Hierarchical Clustering
复制标题
非层次聚类
DOI：
10.1007/978-981-13-0553-5_3
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
F. Martella
中科院分区：
文献类型：
--
作者：
P. Giordani;M. Ferraro;F. Martella
与分层聚类方法不同，非分层聚类方法需要用户事先指定聚类的数量；因此，在这种情况下，得到的是单个分区。两种最著名的非分层聚类算法是k-Means和k-Medoids算法。它们在集群原型的定义上有所不同。特别地，k-Means原型，称为质心，被定义为分配给集群的单位的平均值，而k-Medoids原型，称为中位数，确定每个集群最具代表性的观察单位。在本章中，将从理论角度简要介绍非分层聚类方法，并通过一些现实生活中的案例研究详细介绍它们的实现。
Differently from hierarchical clustering procedures, non-hierarchical clustering methods need the user to specify in advance the number of clusters; therefore, in this case, a single partition is obtained. The two most famous non-hierarchical clustering algorithms are the k-Means and the k-Medoids one. They differ in the definition of the cluster prototypes. In particular, the k-Means prototypes, called centroids, are defined to be the average values of units assigned to the clusters, while the k-Medoids prototypes, called medoids, identify the most representative observed units for each cluster. In this chapter, non-hierarchical clustering methods will be briefly introduced from a theoretical point of view and their implementation will be presented in detail by means of some real-life case studies.
影响因子：
2
作者：
HUBERT, L;ARABIE, P
通讯作者：
ARABIE, P