给定一个数组 --> @x 做聚类分析，现在不知道它能分成多少类，是要做 fuzzy C-means clustering么？
我在R上安装了 e1071，里面有cmeans这个聚类分析工具，但是当我导入并计算的时候，发现这个工具也必须要告诉它分成几类。
请问，如何在R中实现 未知分类数目的聚类分析？
注：Fuzzy C-Means Clustering ----> cmeans Description:
Description
The fuzzy version of the known kmeans clustering algorithm as well as an on-line variant (Unsupervised Fuzzy Competitive learning).
Usage
cmeans(x, centers, iter.max = 100, verbose = FALSE,
dist = "euclidean", method = "cmeans", m = 2,
rate.par = NULL, weights = 1, control = list())
Arguments
x The data matrix where columns correspond to variables and rows to observations.
centers Number of clusters or initial values for cluster centers.
iter.max Maximum number of iterations.
verbose If TRUE, make some output during learning.
dist Must be one of the following: If "euclidean", the mean square error, if "manhattan", the mean absolute error is computed. Abbreviations are also accepted.
method If "cmeans", then we have the c-means fuzzy clustering method, if "ufcl" we have the on-line update. Abbreviations are also accepted.
m A number greater than 1 giving the degree of fuzzification.
rate.par A number between 0 and 1 giving the parameter of the learning rate for the on-line variant. The default corresponds to 0.3.
weights a numeric vector with non-negative case weights. Recycled to the number of observations in x if necessary.
control a list of control parameters. See Details.
我在R上安装了 e1071，里面有cmeans这个聚类分析工具，但是当我导入并计算的时候，发现这个工具也必须要告诉它分成几类。
请问，如何在R中实现 未知分类数目的聚类分析？
注：Fuzzy C-Means Clustering ----> cmeans Description:
Description
The fuzzy version of the known kmeans clustering algorithm as well as an on-line variant (Unsupervised Fuzzy Competitive learning).
Usage
cmeans(x, centers, iter.max = 100, verbose = FALSE,
dist = "euclidean", method = "cmeans", m = 2,
rate.par = NULL, weights = 1, control = list())
Arguments
x The data matrix where columns correspond to variables and rows to observations.
centers Number of clusters or initial values for cluster centers.
iter.max Maximum number of iterations.
verbose If TRUE, make some output during learning.
dist Must be one of the following: If "euclidean", the mean square error, if "manhattan", the mean absolute error is computed. Abbreviations are also accepted.
method If "cmeans", then we have the c-means fuzzy clustering method, if "ufcl" we have the on-line update. Abbreviations are also accepted.
m A number greater than 1 giving the degree of fuzzification.
rate.par A number between 0 and 1 giving the parameter of the learning rate for the on-line variant. The default corresponds to 0.3.
weights a numeric vector with non-negative case weights. Recycled to the number of observations in x if necessary.
control a list of control parameters. See Details.
