factoextra::fviz_gap_stat() 与 factoextra::fviz_nbclust(df, method = "gap_stat")

Question

我想弄清楚为什么 factoextra 包中的这两个函数具有 看似相似的参数（例如 kmeans、gap_stat,k.maxandB`) 产生了不同的结果。

library(cluster)
library(cluster.datasets)
library(tidyverse)
library(factoextra)

# load data and scale it
data("all.mammals.milk.1956")
mammals <- all.mammals.milk.1956 %>% select(-name)
mammals_scaled <- scale(mammals)

第一种方法使用factoextra::clusGap()和factoextra::fviz_gap_stat()

gap_stat <- clusGap(mammals_scaled, FUN = kmeans, K.max = 24, B = 50)

fviz_gap_stat(gap_stat) + theme_minimal() + ggtitle("fviz_gap_stat: Gap Statistic")

第二种方法使用 factoextra::fviz_nbclust() 其中

fviz_nbclust(mammals_scaled, kmeans, method = "gap_stat", k.max = 24, nboot = 50) + theme_minimal() + ggtitle("fviz_nbClust_gap_stat: Gap Statistic")

我认为它可能是 clusGap() 的 nstart 选项，但是当我使用 jimhester/lookup 阅读 fviz_nbclust() 的源代码时，代码如下我找不到问题所在：

devtools::install_github("jimhester/lookup")
lookup::lookup(fviz_nbclust)


function (x, FUNcluster = NULL, method = c("silhouette", "wss", 
        "gap_stat"), diss = NULL, k.max = 10, nboot = 100, verbose = interactive(), 
        barfill = "steelblue", barcolor = "steelblue", linecolor = "steelblue", 
        print.summary = TRUE, ...) 
{
        set.seed(123)
        if (k.max < 2) 
                stop("k.max must bet > = 2")
        method = match.arg(method)
        if (!inherits(x, c("data.frame", "matrix")) & !("Best.nc" %in% 
                names(x))) 
                stop("x should be an object of class matrix/data.frame or ", 
                        "an object created by the function NbClust() [NbClust package].")
        if (inherits(x, "list") & "Best.nc" %in% names(x)) {
                best_nc <- x$Best.nc
                if (class(best_nc) == "numeric") 
                        print(best_nc)
                else if (class(best_nc) == "matrix") 
                        .viz_NbClust(x, print.summary, barfill, barcolor)
        }
        else if (is.null(FUNcluster)) 
                stop("The argument FUNcluster is required. ", "Possible values are kmeans, pam, hcut, clara, ...")
        else if (method %in% c("silhouette", "wss")) {
                if (is.data.frame(x)) 
                        x <- as.matrix(x)
                if (is.null(diss)) 
                        diss <- stats::dist(x)
                v <- rep(0, k.max)
                if (method == "silhouette") {
                        for (i in 2:k.max) {
                                clust <- FUNcluster(x, i, ...)
                                v[i] <- .get_ave_sil_width(diss, clust$cluster)
                        }
                }
                else if (method == "wss") {
                        for (i in 1:k.max) {
                                clust <- FUNcluster(x, i, ...)
                                v[i] <- .get_withinSS(diss, clust$cluster)
                        }
                }
                df <- data.frame(clusters = as.factor(1:k.max), y = v)
                ylab <- "Total Within Sum of Square"
                if (method == "silhouette") 
                        ylab <- "Average silhouette width"
                p <- ggpubr::ggline(df, x = "clusters", y = "y", group = 1, 
                        color = linecolor, ylab = ylab, xlab = "Number of clusters k", 
                        main = "Optimal number of clusters")
                if (method == "silhouette") 
                        p <- p + geom_vline(xintercept = which.max(v), linetype = 2, 
                                color = linecolor)
                return(p)
        }
        else if (method == "gap_stat") {
                extra_args <- list(...)
                gap_stat <- cluster::clusGap(x, FUNcluster, K.max = k.max, 
                        B = nboot, verbose = verbose, ...)
                if (!is.null(extra_args$maxSE)) 
                        maxSE <- extra_args$maxSE
                else maxSE <- list(method = "firstSEmax", SE.factor = 1)
                p <- fviz_gap_stat(gap_stat, linecolor = linecolor, 
                        maxSE = maxSE)
                return(p)
        }
}

Answer 1

区别就在 fviz_nbclust 函数的开头。在第 6 行中设置了随机种子：
set.seed(123)

因为 kmeans 算法使用随机开始，所以在重复运行时结果可能不同。例如，我将您的数据与两个不同的随机种子一起使用，得出的结果略有不同。

set.seed(123)  
gap_stat <- cluster::clusGap(mammals_scaled, FUN = kmeans, K.max = 24, B = 50)   
fviz_gap_stat(gap_stat) + theme_minimal() + ggtitle("fviz_gap_stat: Gap Statistic")

seed 123 gap stat

set.seed(42)  
gap_stat <- cluster::clusGap(mammals_scaled, FUN = kmeans, K.max = 24, B = 50)
fviz_gap_stat(gap_stat) + theme_minimal() + ggtitle("fviz_gap_stat: Gap Statistic")

seed 42 gap stat

我不完全确定为什么种子 123 结果不一样，但我认为这与以下事实有关：在我的代码中，它是在 clusGap 函数上方执行的，在 Fviz_nbclust 其他几个函数中命令在两者之间进行评估。

factoextra::fviz_gap_stat() 与 factoextra::fviz_nbclust(df, method = "gap_stat")

factoextra::fviz_gap_stat() versus factoextra::fviz_nbclust(df, method = "gap_stat")

r

cluster-analysis