vegclust(vegclust)
vegclust()所属R语言包：vegclust
Vegetation clustering methods
植被的聚类方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs hard or fuzzy clustering of a community data matrix
执行硬或社区数据矩阵的模糊聚类
用法----------Usage----------
vegclust(x, mobileCenters, fixedCenters = NULL, method="NC", m = 2, dnoise = NULL, eta = NULL, alpha=0.001, iter.max=100, nstart=1, maxminJ = 10, seeds=NULL, verbose=FALSE)
vegclustdist(x, mobileMemb, fixedMemb = NULL, method="NC", m = 2, dnoise = NULL, eta = NULL, alpha=0.001, iter.max=100, nstart=1, seeds=NULL)
参数----------Arguments----------
参数：x
Community data. A site-by-species matrix or data frame (for vegclust) or a site-by-site dissimilarity matrix or dist object (for vegclustdist).
社区数据。一个站点的品种矩阵或数据框（用于vegclust）的网站现场相异矩阵或dist对象（vegclustdist）。
参数：mobileCenters
A number, a vector of seeds, or coordinates for mobile clusters.
一个数，种子的向量，或用于移动聚类的坐标。
参数：fixedCenters
A matrix or data frame with coordinates for fixed (non-mobile) clusters.
矩阵或数据框的坐标为固定的（不可移动）聚类。
参数：mobileMemb
A number, a vector of seeds, or starting memberships for mobile clusters.
一个数字，一个向量的种子，或从移动聚类中的成员身份。
参数：fixedMemb
A matrix or data frame with starting memberships for fixed (non-mobile) clusters.
矩阵或数据框开始固定（不可移动）聚类中的成员身份。
参数：method
A clustering model. Current accepted models are: "KM" for kmeans (MacQueen 1967), "FCM" for fuzzy c-means (Bezdek 1981), "NC" for noise clustering (Dave and Krishnapuram 1997) and "PCM" for possibilistic c-means (Krishnapuram and Keller 1993).
聚类分析模型。目前接受的模式是："KM"的kmeans（MacQueen 1967），"FCM"模糊C-均值（贝兹德克1981年），"NC"噪声聚类（Dave和Krishnapuram 1997）和<X >可能性c-均值（Krishnapuram和Keller，1993）。
参数：m
The fuzziness exponent to be used (this is relevant for all models except for kmeans)
使用的模糊性指数（这是所有车型的相关的kmeans除外）
参数：dnoise
The distance to the noise cluster, relevant for noise clustering (NC).
距离的噪音聚类，相关的噪声聚类（NC）。
参数：eta
A vector of reference distances, relevant for possibilistic C-means (PCM).
一个向量的参考距离，相关的可能性C均值（PCM）。
参数：alpha
Threshold used to stop iterations. The maximum difference in the membership matrix of the current vs. the previous iteration will be compared to this value.
阈值停止迭代。将这个值进行比较的电流与前一次迭代的隶属度矩阵的最大差值。
参数：iter.max
The maximum number of iterations allowed.
允许的最大数目的迭代。
参数：nstart
If mobileCenters or mobileMemb is a number, how many random sets should be chosen?
如果mobileCenters或mobileMemb是一个数字，有多少随机集，应选择呢？
参数：maxminJ
When random starts are used, these will stop if at least maxminJ runs ended up in the same functional value.
当使用随机启动，这些都将停止，如果至少maxminJ运行结束了相同的功能价值。
参数：seeds
If mobileCenters or mobileMemb is a number, a vector indicating which objects are potential initial centers. If NULL all objects are valid seeds.
如果mobileCenters或mobileMemb是一个数字，一个矢量对象是潜在的初始中心。 NULL如果所有的对象都是有效的种子。
参数：verbose
Flag to print extra output.
标志来打印额外的输出。
Details
详细信息----------Details----------
Functions vegclust and vegclustdist try to generalize the kmeans function in stats in three ways. Firstly, they allows different clustering models (three fuzzy and one hard). The reader should refer to the original publications to better understand the differences between models. Secondly, users can specify fixed clusters (that is, centroids that do not change their positions during iterations). Fixed clusters are intended to be used when some clusters were previously defined and new data has been collected. One may allow some of these new data points to form new clusters, while some other points will be assigned to the original clusters. In the case of models with cluster repulsion (such as KM, FCM or NC) the new (mobile) clusters are not allowed to 'push' the fixed ones. As a result, mobile clusters will occupy new regions of the reference space. Thirdly, vegclustdist implements the distance-based equivalent of vegclust. Note that all data frames or matrices used as input of vegclust should be defined on the same space of species (see conformveg). Unlike kmeans, which allows different specific algorithms, here updates of centroids are done after all objects have been reassigned (Forgy 1965). In order to obtain hard cluster definitions, users can apply the function defuzzify to the vegclust object.
功能vegclust和vegclustdist尝试概括kmeans功能，stats在三个方面。首先，它们允许不同的聚类分析模型（三个模糊和一个硬盘）。读者可以参考原来的出版物，以更好地了解模型之间的差异。其次，用户可以指定固定的簇（即，不改变它们的位置在迭代过程中的质心）。固定的聚类时要使用的一些聚类先前定义和新的数据被收集。一个可允许一些这些新的数据点，以形成新的聚类，而其他一些点，将被分配到原来的簇。新的（移动）聚类在聚类排斥（如KM，FCM NC）模型的情况下，不允许“推”固定的。其结果是，移动聚类将占据的基准空间的新的区域。第三，vegclustdist实现了基于距离的vegclust相当于。请注意，所有的数据框或矩阵的作为vegclust输入应该定义在同一个空间的品种（见conformveg“）。不同的kmeans，这使得不同的特定的算法，这里的质心做更新后的所有对象已被重新分配（Forgy 1965年）。为了获得硬的聚类定义，用户可以应用的功能defuzzify到vegclust对象的。
值----------Value----------
Returns an object of type vegclust with the following items: <table summary="R valueblock"> <tr valign="top"><td>mode</td> <td> raw for function vegclust and dist for function vegclustdist.</td></tr> <tr valign="top"><td>method</td> <td> The clustering model used</td></tr> <tr valign="top"><td>m</td> <td> The fuzziness exponent used (m=1 in case of kmeans)</td></tr> <tr valign="top"><td>dnoise</td> <td> The distance to the noise cluster used for noise clustering (NC). This is set to NULL for other models.</td></tr> <tr valign="top"><td>eta</td> <td> The reference distance vector used for possibilistic c-means (PCM). This is set to NULL for other models.</td></tr> <tr valign="top"><td>memb</td> <td> The fuzzy membership matrix. Columns starting with "M" indicate mobile clusters, whereas columns starting with "F" indicate fixed clusters.</td></tr> <tr valign="top"><td>mobileCenters</td> <td> A data frame with the coordinates of the mobile centers (not available in vegclustdist).</td></tr> <tr valign="top"><td>fixedCenters</td> <td> A data frame with coordinates for fixed (non-mobile) clusters (not available in vegclustdist).</td></tr> <tr valign="top"><td>dist2clusters</td> <td> The matrix of object distances to cluster centers. Columns starting with "M" indicate mobile clusters, whereas columns starting with "F" indicate fixed clusters.</td></tr> <tr valign="top"><td>withinss</td> <td> The within-cluster sum of squares for each cluster.</td></tr> <tr valign="top"><td>size</td> <td> The number of objects belonging to each cluster. In case of fuzzy clusters the sum of memberships is given.</td></tr> <tr valign="top"><td>functional</td> <td> The objective function value (the minimum value attained after all iterations).</td></tr> </table>
返回一个类型的对象vegclust下列项目：<table summary="R valueblock"> <tr valign="top"> <TD> mode </ TD> <TD><X >函数raw和vegclust功能dist。</ TD> </ TR> <tr valign="top"> <TD>vegclustdist</ TD <TD>使用聚类分析模型</ TD> </ TR> <tr valign="top"> <TD>method </ TD> <TD>的模糊性指数（m情况的kmeans）</ TD> </ TR> <tr valign="top"> <TD>m=1</ TD> <TD>距离的噪音聚类使用的噪声聚类（NC）。这是设置为dnoise其他型号的。</ TD> </ TR> <tr valign="top"> <TD>NULL </ TD> <TD>的参考距离矢量用于的可能性c的手段（PCM）。这是设置为eta其他型号的。</ TD> </ TR> <tr valign="top"> <TD>NULL </ TD> <TD>的模糊隶属度矩阵。 “M”表示列，而列开始与“F”表示固定的聚类移动聚类。</ TD> </ TR> <tr valign="top"> <TD> memb </ TD> < TD移动中心的坐标（不mobileCenters）</ TD> </ TR> <tr valign="top"> <TD>vegclustdist</ TD> A的数据框<td>一个数据框与固定（不可移动）聚类（不fixedCenters）</ TD> </ TR> <tr valign="top"> <TD><X坐标> </ TD> <TD>对象的距离，聚类中心的矩阵。 “M”表示列，而列开始与“F”表示固定的聚类移动聚类。</ TD> </ TR> <tr valign="top"> <TD> vegclustdist </ TD> < TD>聚类内为每个聚类的平方之和。</ TD> </ TR> <tr valign="top"> <TD>dist2clusters </ TD> <TD>的对象的数量属于每个聚类。在模糊聚类的会员资格和</ TD> </ TR> <tr valign="top"> <TD>withinss </ TD> <TD>的目标函数值（最低。所有迭代后达到的值）。</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Miquel De C谩ceres, Forest Science Center of Catalonia
参考文献----------References----------
Forgy, E. W. (1965) Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Biometrics 21, 768-769.
MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam and J. Neyman, 1, pp. 281-297. Berkeley, CA: University of California Press.
Dav茅, R. N. and R. Krishnapuram (1997) Robust clustering methods: a unified view. IEEE Transactions on Fuzzy Systems 5, 270-293.
Bezdek, J. C. (1981) Pattern recognition with fuzzy objective functions. Plenum Press, New York.
Krishnapuram, R. and J. M. Keller. (1993) A possibilistic approach to clustering. IEEE transactions on fuzzy systems 1, 98-110.
De C谩ceres, M., Font, X, Oliva, F. (2010) The management of numerical vegetation classifications with fuzzy clustering methods. Journal of Vegetation Science 21 (6): 1138-1151.
参见----------See Also----------
kmeans, cmeans,vegclass,defuzzify
kmeans，cmeans，vegclass，defuzzify
实例----------Examples----------
# Loads data [数据加载]
data(wetland)
# This equals the chord transformation (see also 'normalize' option in \code{\link{decostand}} from the vegan package)[这等于和弦转换（参见标准化选项\ {\的链接{decostand}}从素食包的代码）]
wetland.chord = as.data.frame(sweep(as.matrix(wetland), 1, sqrt(rowSums(as.matrix(wetland)^2)), "/"))
# Create noise clustering with 3 clusters. Perform 10 starts from random seeds and keep the best solution[创建噪音聚类与3类。进行10次随机种子开始，并保持最佳的解决方案]
wetland.nc = vegclust(wetland.chord, mobileCenters=3, m = 1.2, dnoise=0.75, method="NC", nstart=10)
# Fuzzy membership matrix[模糊隶属度矩阵]
wetland.nc$memb
# Cardinality of fuzzy clusters (i.e., the number of objects belonging to)[模糊聚类的基数（即对象的数量）]
wetland.nc$size
# Obtains hard membership vector, with 'N' for objects that are unclassified[获得硬会员向量，以N未分类的对象，]
defuzzify(wetland.nc$memb)$cluster
# The same result is obtained with a matrix of chord distances[得到相同的结果的矩阵和弦距离]
wetland.d = dist(wetland.chord)
wetland.d.nc = vegclustdist(wetland.d, mobileMemb=3, m = 1.2, dnoise=0.75, method="NC", nstart=10)
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。
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发表于 2012-10-1 15:17:00
