pam.object(cluster)
pam.object()所属R语言包：cluster
Partitioning Around Medoids (PAM) Object
围绕中心点（PAM）对象分区
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The objects of class "pam" represent a partitioning of a dataset into clusters.
类的对象"pam"代表到集群的数据集分区。
值----------Value----------
A legitimate pam object is a list with the following components:
一个合法的pam对象是一个具有下列组件的列表：
参数：medoids
the medoids or representative objects of the clusters. If a dissimilarity matrix was given as input to pam, then a vector of numbers or labels of observations is given, else medoids is a matrix with in each row the coordinates of one medoid.
集群的中心点或代表对象。如果相异矩阵作为输入pam的，然后观测的数字或标签的向量，否则medoids是一个矩阵中的每一行一个medoid坐标。
参数：id.med
integer vector of indices giving the medoid observation numbers.
给予medoid观察数字指数的整数向量。
参数：clustering
the clustering vector, see partition.object.
聚类向量，看到partition.object。
参数：objective
the objective function after the first and second step of the pam algorithm.
后pam算法的第一步和第二步的目标函数。
参数：isolation
vector with length equal to the number of clusters, specifying which clusters are isolated clusters (L- or L*-clusters) and which clusters are not isolated.<br> A cluster is an L*-cluster iff its diameter is smaller than its separation. A cluster is an L-cluster iff for each observation i the maximal dissimilarity between i and any other observation of the cluster is smaller than the minimal dissimilarity between i and any observation of another cluster. Clearly each L*-cluster is also an L-cluster.
指定矢量长度等于集群，该集群是孤立的簇（L型或L *集群）和集群不是孤立的。参考集群是一个L *集群，当且仅当它的直径比其小分离。集群是每个观察我，我和任何其他观测集群之间的最大相异小于最小相异我和任何其他群集的观察之间的L型集群IFF。显然，每个L *集群也是一个L集群。
参数：clusinfo
matrix, each row gives numerical information for one cluster. These are the cardinality of the cluster (number of observations), the maximal and average dissimilarity between the observations in the cluster and the cluster's medoid, the diameter of the cluster (maximal dissimilarity between two observations of the cluster), and the separation of the cluster (minimal dissimilarity between an observation of the cluster and an observation of another cluster).
矩阵，每一行给出一个群集的数字信息。这些集群的基数（若干意见），在该集群中的观察和群集的medoid，直径的集群（2群集的意见之间的最大相异）之间的最大和平均相异，和分离的集群（集群的观察和观察另一个集群之间的最小相异）。
参数：silinfo
list with silhouette width information, see partition.object.
轮廓宽度信息的列表，请参阅partition.object。
参数：diss
dissimilarity (maybe NULL), see partition.object.
相异（可能为空），请参阅partition.object。
参数：call
generating call, see partition.object.
产生呼叫，看到partition.object。
参数：data
(possibibly standardized) see partition.object.
（possibibly标准化）看到partition.object。
发电----------GENERATION----------
These objects are returned from pam.
从pam这些对象返回。
方法----------METHODS----------
The "pam" class has methods for the following generic functions: print, summary.
"pam"类有以下的通用功能的方法：print，summary。
遗传----------INHERITANCE----------
The class "pam" inherits from "partition". Therefore, the generic functions plot and clusplot can be used on a pam object.
从"pam"类"partition"继承。因此，通用的功能plot和clusplot可以pam对象的使用。
参见----------See Also----------
pam, dissimilarity.object, partition.object, plot.partition.
pam，dissimilarity.object，partition.object，plot.partition。
举例----------Examples----------
## Use the silhouette widths for assessing the best number of clusters,[＃用于评估集群的最佳轮廓宽度，]
## following a one-dimensional example from Christian Hennig :[＃从基督教亨尼格一维的例子：]
##[＃]
x <- c(rnorm(50), rnorm(50,mean=5), rnorm(30,mean=15))
asw <- numeric(20)
## Note that "k=1" won't work![＃注意，“K = 1”，将无法正常工作！]
for (k in 2:20)
asw[k] <- pam(x, k) $ silinfo $ avg.width
k.best <- which.max(asw)
cat("silhouette-optimal number of clusters:", k.best, "\n")
plot(1:20, asw, type= "h", main = "pam() clustering assessment",
xlab= "k (# clusters)", ylab = "average silhouette width")[集群）“，ylab =”平均轮廓宽度“）]
axis(1, k.best, paste("best",k.best,sep="\n"), col = "red", col.axis = "red")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-16 21:52:32
