Как нарисовать график внутри кластерных сумм квадратов для кластера?

Question

У меня есть кластерный график R, в то время как я хочу оптимизировать «критерий локтя» кластеризации с помощью wss-графика, но я не знаю, как нарисовать wss-график для кластера, кто-нибудь мне поможет?

Вот мои данные:

Friendly<-c(0.467,0.175,0.004,0.025,0.083,0.004,0.042,0.038,0,0.008,0.008,0.05,0.096) 
Polite<-c(0.117,0.55,0,0,0.054,0.017,0.017,0.017,0,0.017,0.008,0.104,0.1) 
Praising<-c(0.079,0.046,0.563,0.029,0.092,0.025,0.004,0.004,0.129,0,0,0,0.029) 
Joking<-c(0.125,0.017,0.054,0.383,0.108,0.054,0.013,0.008,0.092,0.013,0.05,0.017,0.067) 
Sincere<-c(0.092,0.088,0.025,0.008,0.383,0.133,0.017,0.004,0,0.063,0,0,0.188) 
Serious<-c(0.033,0.021,0.054,0.013,0.2,0.358,0.017,0.004,0.025,0.004,0.142,0.021,0.108) 
Hostile<-c(0.029,0.004,0,0,0.013,0.033,0.371,0.363,0.075,0.038,0.025,0.004,0.046) 
Rude<-c(0,0.008,0,0.008,0.017,0.075,0.325,0.313,0.004,0.092,0.063,0.008,0.088) 
Blaming<-c(0.013,0,0.088,0.038,0.046,0.046,0.029,0.038,0.646,0.029,0.004,0,0.025) 
Insincere<-c(0.075,0.063,0,0.013,0.096,0.017,0.021,0,0.008,0.604,0.004,0,0.1) 
Commanding<-c(0,0,0,0,0,0.233,0.046,0.029,0.004,0.004,0.538,0,0.146) 
Suggesting<-c(0.038,0.15,0,0,0.083,0.058,0,0,0,0.017,0.079,0.133,0.442) 
Neutral<-c(0.021,0.075,0.017,0,0.033,0.042,0.017,0,0.033,0.017,0.021,0.008,0.717) 

data <- data.frame(Friendly,Polite,Praising,Joking,Sincere,Serious,Hostile,Rude,Blaming,Insincere,Commanding,Suggesting,Neutral) 

И вот мой код кластеризация:

cor <- cor (data) 
dist<-dist(cor) 
hclust<-hclust(dist) 
plot(hclust) 

И получите дендрограммы после выполнения кода выше, в то время как я могу нарисовать сюжет как это:

Answer 1

Если я следую за то, что вы хотите, то п нам нужна функция для вычисления WSS

wss <- function(d) { 
    sum(scale(d, scale = FALSE)^2) 
} 

и обертку для этой wss() функции

wrap <- function(i, hc, x) { 
    cl <- cutree(hc, i) 
    spl <- split(x, cl) 
    wss <- sum(sapply(spl, wss)) 
    wss 
} 

Эта оболочка имеет следующие аргументы, входы:

• i числа кластеров вырезать данные в
• hc объект иерархического кластерного анализа
• x исходные данные

wrap обрезает дендрограммы в i кластеров, разбивает исходные данные в составе кластера, дается cl и вычисляет WSS для каждого кластера. Эти значения WSS суммируются, чтобы дать WSS для этой кластеризации.

Бежим все это с помощью sapply по числу кластеров 1, 2, ..., nrow(data)

res <- sapply(seq.int(1, nrow(data)), wrap, h = cl, x = data) 

screeplot можно сделать с помощью

plot(seq_along(res), res, type = "b", pch = 19) 

Вот пример использования известный набор данных Эдгара Андерсона Ириса:

iris2 <- iris[, 1:4] # drop Species column 
cl <- hclust(dist(iris2), method = "ward.D") 

## Takes a little while as we evaluate all implied clustering up to 150 groups 
res <- sapply(seq.int(1, nrow(iris2)), wrap, h = cl, x = iris2) 
plot(seq_along(res), res, type = "b", pch = 19) 

Это дает:

Мы можем увеличить, просто показывая первые 1:50 кластеры

plot(seq_along(res[1:50]), res[1:50], type = "o", pch = 19) 

который дает

Вы можете ускорить основной шаг вычислений либо запустив sapply() через соответствующую параллельную альтернативу или просто сделайте вычисление для меньшего количества чем nrow(data) кластеров, т.е.

res <- sapply(seq.int(1, 50), wrap, h = cl, x = iris2) ## 1st 50 groups