PAM（Partitioning Around Medoid,围绕中心点的划分）算法是是划分算法中一种很重要的算法，有时也称为k-中心点算法，是指用中心点来代表一个簇。PAM算法最早由Kaufman和Rousseevw提出，Medoid的意思就是位于中心位置的对象。PAM算法的目的是对n个数据对象给出k个划分。PAM算法的基本思想：PAM算法的目的是对成员集合D中的N个数据对象给出k个划分，形成k个簇，在每个簇中随机选取1个成员设置为中心点，然后在每一步中，对输入数据集中目前还不是中心点的成员根据其与中心点的相异度或者距离进行逐个比较，看是否可能成为中心点。用簇中的非中心点到簇的中心点的所有距离之和来度量聚类效果，其中成员总是被分配到离自身最近的簇中，以此来提高聚类的质量。-PAM (Partitioning Around Medoid Around the division of the center,) algorithm is a kind of partition algorithm is very important algorithm, and sometimes also called k-center algorithm, it is to point to in the center to represent a cluster. The earliest PAM algorithm by Kaufman and Rousseevw puts forward, Medoid mean is at the center of the location of the object. PAM algorithm for the purpose of n data object is given k division. PAM algorithm to the basic idea of the: PAM algorithm for the purpose of members set D is the N data object given k division, forming k cluster, each cluster in selected at random from a members set to center, then at each step, the focus of the input data is not a member of the center according to the center YiDu or phase with each distance is, look to whether can be centered. Use cluster in the center point to the center of the cluster of the sum of all the distance to measure the clustering effect, which is always assigned members from their recent cluste