聚类分析有很多种， 效果好不好大概要根据数据特征来确定。最常见的是kmeans法聚类
> setwd("D:\\R_test")
> data_in <- read.delim("tmp_result.txt", header=T)
> fit <- kmeans(data_in, 3)
> library(cluster)
> clusplot(data_in, fit$cluster, color=T, shade=T, labels = 2, lines =0)也可以用mclust
> install.packages("mclust")
试开URL’http://cloud.r-project.org/bin/windows/contrib/2.14/mclust_4.0.zip'
Content type 'application/zip' length 2371233 bytes (2.3 Mb)
打开了URL
downloaded 2.3 Mb
程序包‘mclust’打开成功，MD5和检查也通过
下载的程序包在
C:\Users\Administrator\AppData\Local\Temp\RtmpiIyw2o\downloaded_packages里
> fit <- Mclust(data_in)
> summary(fit)
----------------------------------------------------
Gaussian finite mixture model fitted by EM algorithm
----------------------------------------------------
Mclust XXX (elliposidal multivariate normal) model with 1 component:
log.likelihood n df BIC
1616504 263 33410 3046843
Clustering table:
1
263
> fit$ // 按下Tab键，有以下选项
fit$call fit$modelName fit$n fit$d fit$G
fit$BIC fit$bic fit$loglik fit$df fit$parameters
fit$classification fit$uncertainty
> plot(fit, what="classification")
// http://www.statmethods.net/advstats/cluster.html
