R 如何运行并行弯头方法找到合适的k-簇
“data.clustering”数据框,大小:943x2R 如何运行并行弯头方法找到合适的k-簇,r,parallel-processing,k-means,R,Parallel Processing,K Means,“data.clustering”数据框,大小:943x2 > head(data.clustering) age gender 2 2 1 3 6 2 4 2 1 5 2 1 6 6 2 7 6 1 当我使用弯头方法找到k值时: elbow.k <- function(mydata){ ## determine a "good" k using elbow dist.obj <-
> head(data.clustering)
age gender
2 2 1
3 6 2
4 2 1
5 2 1
6 6 2
7 6 1
当我使用弯头方法找到k值时:
elbow.k <- function(mydata){
## determine a "good" k using elbow
dist.obj <- dist(mydata);
hclust.obj <- hclust(dist.obj);
css.obj <- css.hclust(dist.obj,hclust.obj);
elbow.obj <- elbow.batch(css.obj);
# print(elbow.obj)
k <- elbow.obj$k
return(k)
}
# find k value
start.time <- Sys.time();
k.clusters <- elbow.k(data.clustering);
end.time <- Sys.time();
cat('Time to find k using Elbow method is',(end.time - start.time),'seconds with k value:', k.clusters);
The time is so large:
Time to find k using Elbow method is 24.01472 seconds with k value: 10
在R.中可以使用库(并行)包,但必须考虑使用CuultValueQuo()、CultExExtPoE()将变量和包导入到您的环境中。
我认为您的代码如下:
图书馆(平行)
#
在R.中可以使用库(并行)包,但必须考虑使用CuultValueQuo()、CultExExtPoE()将变量和包导入到您的环境中。
我认为您的代码如下:
图书馆(平行)
#
弯头.k下面是一个共享内存并行示例,它使用k-means创建弯头图
library(parallel)
elbow <- function(min_max, frame) {
set.seed(42)
wss <- (nrow(frame)-1)*sum(apply(frame,2,var))
for (i in min_max) {
wss[i] <- sum(kmeans(frame,centers=i,algorithm = c('MacQueen'))$withinss)
}
return(wss)
}
parallel_elbow <- function(kmax, frame_choice) {
# create separate kmin:kmax vectors
cut_point <- 3
centers_vec <- 2:kmax
x <- seq_along(centers_vec)
chunks <- split(centers_vec, ceiling(x/cut_point))
# use shared-memory parallelism on function of choice
results <- mclapply(chunks, FUN=elbow, frame=frame_choice)
# gather the results of each parallel run
no_nas <- list()
for(i in 1:length(results)) {
no_nas[i] <- list(as.numeric(na.omit(results[[i]])))
}
vec <- unlist(no_nas)
final_vec <- setdiff(vec, vec[1])
final_vec <- append(vec[1],final_vec)
# create scree plot of all wss values
plot(1:length(final_vec), final_vec, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares", pch = 16, main="Elbow Plot", col="steelblue")
}
将文档术语矩阵上的运行时与2176个文档进行比较:
system.time(elbow(1:10, dtm))
user system elapsed
83.130 1.450 84.843
system.time(parallel_elbow(10, dtm))
user system elapsed
21.097 0.653 48.132
橙色表示正常,蓝色表示平行
下面是一个共享内存并行示例,它使用k-means创建弯头图
library(parallel)
elbow <- function(min_max, frame) {
set.seed(42)
wss <- (nrow(frame)-1)*sum(apply(frame,2,var))
for (i in min_max) {
wss[i] <- sum(kmeans(frame,centers=i,algorithm = c('MacQueen'))$withinss)
}
return(wss)
}
parallel_elbow <- function(kmax, frame_choice) {
# create separate kmin:kmax vectors
cut_point <- 3
centers_vec <- 2:kmax
x <- seq_along(centers_vec)
chunks <- split(centers_vec, ceiling(x/cut_point))
# use shared-memory parallelism on function of choice
results <- mclapply(chunks, FUN=elbow, frame=frame_choice)
# gather the results of each parallel run
no_nas <- list()
for(i in 1:length(results)) {
no_nas[i] <- list(as.numeric(na.omit(results[[i]])))
}
vec <- unlist(no_nas)
final_vec <- setdiff(vec, vec[1])
final_vec <- append(vec[1],final_vec)
# create scree plot of all wss values
plot(1:length(final_vec), final_vec, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares", pch = 16, main="Elbow Plot", col="steelblue")
}
将文档术语矩阵上的运行时与2176个文档进行比较:
system.time(elbow(1:10, dtm))
user system elapsed
83.130 1.450 84.843
system.time(parallel_elbow(10, dtm))
user system elapsed
21.097 0.653 48.132
橙色表示正常,蓝色表示平行
回答不错,你能提供一些关于弯头方法的评论,以便更好地理解整个方法吗?回答不错,你能提供一些关于弯头方法的评论,以便更好地理解整个方法吗。