R中cluster中包含多种聚类算法，下面通过某个数据集，进行三种聚类算法的评估
# ============================ # 评估聚类 # # ============================ # 引入fpc包(cluster.stats) library(fpc) # 引入包库(clara、fanny) library(cluster) #=====调用聚类算法======================================================= # 确定簇心个数 cluster_num <- 3 # 读取数据 data <- read.csv("data.csv",header = T) # 调用kmeans算法 km <- kmeans(data,cluster_num) # 调用(clara)算法 cl <- clara(data,cluster_num) # 调用模糊C-Means聚类算法 fan <- fanny(data,cluster_num) #=====调用聚类算法======================================================= # 聚类评价统计量 km_stats <- cluster.stats(dist(data), km$cluster) cl_stats <- cluster.stats(dist(data), cl$cluster) fcm_stats <- cluster.stats(dist(data), fan$clustering) # 信息数据框表化 info <- data.frame( Algorithm = c("KMeans", "Clara", "FCM"), Silwidth = c(km_stats$avg.silwidth,cl_stats$avg.silwidth,fcm_stats$avg.silwidth), AverageWithin = c(km_stats$average.within, cl_stats$average.within, fcm_stats$average.within), averageBetween = c(km_stats$average.between, cl_stats$average.between, fcm_stats$average.between), ch = c(km_stats$ch, cl_stats$ch, fcm_stats$ch) ) # 重命名字段 names(info)[2:5] <- c("轮廓系数","簇内平均距离","簇间平均聚类","Calinski和Harabasz指数")
