Civilized_Spectral_Clustering(SamSPECTRAL)
Civilized_Spectral_Clustering()所属R语言包：SamSPECTRAL
Runs the spectral clustering algorithm on the sample points.
运行样本点的谱聚类算法。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The representatives of communities are considered as the vertices of a graph. Assuming the edges have been weighted according to the equivalent conductance between them, this function runs the classic spectral clustering on the graph.
社区的代表，被视为一个图的顶点。假设加权边缘已根据它们之间的等效电导，这个功能运行经典的谱聚类图。
用法----------Usage----------
Civilized_Spectral_Clustering(full, maximum.number.of.clusters, society, conductance,iterations=200, number.of.clusters=NA,
eigenvalues.num =NA, talk=TRUE,stabilizer=1000)
参数----------Arguments----------
参数：full
The matrix containing the coordinates of all data points.
矩阵包含的所有数据点的坐标。
参数：maximum.number.of.clusters
This parameter is used for fitting the regression line.
此参数用于拟合回归直线。
参数：number.of.clusters
The default value is NA which leads to computating the number of spectral clusters automatically, otherwise this number will determine the number of spectral clusters.
默认值是NA，从而导致光谱聚类computating自动，否则这个数字将决定光谱聚类。
参数：society
The list of communities.
社区名单。
参数：conductance
A matrix in which each entry is the conductance between two communities.
A矩阵中的每个条目是两族之间的电导。
参数：iterations
Number of iterations for the k-means algorithm used by the spectral procedure. 200 is an appropriate value.
由光谱的过程中所使用的k-means算法的迭代数目。 200是一个适当的值。
参数：talk
A boolean flag with default value TRUE. Setting it to FALSE will keep running the procedure quite with no messages.
一个布尔标志使用默认值true。设置为false，将继续运行程序相当没有消息。
参数：eigenvalues.num
An integer with default value NA which prevents ploting the curve of eigenvalues. Otherwise, they will be ploted upto this number.
与防止作图的曲线特征值的默认值的NA整数。否则，他们将ploted高达这个数字。
参数：stabilizer
The larger this integer is, the final results will be more stable because the underlying kmeans will restart many more times.
这个整数是较大的，最后的结果会更加稳定，因为底层的KMEANS将重新启动很多倍。
值----------Value----------
参数：labels.for_num.of.clusters
The k'th element of this list is a vector containing the labels as result of clustering to k parts.
此列表的第k个元素是一个向量，作为结果的聚类的k部分标签。
参数：number.of.clusters
A list containing the desired cluster numbers.
一个列表，其中包含所需的聚类。
参数：eigen.space
The eigen vectors and eigen values of the normalized adjacency matrix computed for spectral clustering.
谱聚类归邻接矩阵的特征向量和特征值计算。
作者（S）----------Author(s)----------
Parisa Shooshtari and Habil Zare
参考文献----------References----------
<h3>See Also</h3> <code>SamSPECTRAL</code>
举例----------Examples----------
## Not run: [＃无法运行：]
library(SamSPECTRAL)
# Reading data file which has been transformed using log transform[读取数据文件已使用log变换转化]
data(small_data)
full <- small
# Parameters:[参数：]
m <- 3000; ns <- 200; sl <- 3; cwt <-1; precision <- 6; mnc <-30
# Sample the data and build the communities[样本数据，并建立社区]
society <- Building_Communities(full=full,m=m, space.length=sl, community.weakness.threshold=cwt)
# Compute conductance between communities[计算社区之间的电导]
conductance <- Conductance_Calculation(full=full, normal.sigma=ns, space.length=sl, society=society, precision=precision)
# Use spectral clustering to cluster the data[使用聚类数据谱聚类]
# First example:[第一个例子：]
clust_result <- Civilized_Spectral_Clustering(full=full, maximum.number.of.clusters=mnc, society=society, conductance=conductance)
number.of.clusters <- clust_result@number.of.clusters
labels.for_num.of.clusters <- clust_result@labels.for_num.of.clusters
L <- labels.for_num.of.clusters[[number.of.clusters]]
# plot(full, pch='.', col= L)[图（完整，PCH =。，COL =）]
# Second example:[第二个例子：]
number.of.clusters <- c(35,20)
# This is faster than runnig Civilized_Spectral_Clustering() twice because the eigen space is not needed to be computed again. [这是速度比两次runnig Civilized_Spectral_Clustering（），因为不需要重新计算特征空间。]
clust_result.not.automatic <-
Civilized_Spectral_Clustering(full=full, society=society, conductance=conductance, number.of.clusters =number.of.clusters)
labels.for_num.of.clusters <- clust_result.not.automatic@labels.for_num.of.clusters
L35 <- labels.for_num.of.clusters[[35]]
L20 <- labels.for_num.of.clusters[[20]]
# plot(full, pch='.', col= L35)[图（完整，PCH =。，COL = L35）]
## End(Not run)[＃结束（不运行）]
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 2012-2-26 13:50:10
