如何运行平行 Elbow 方法找到合适的 k-clusters

Question

大小为"data.clustering"的数据框：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 Elbow 方法找到 k 个值时：

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

谁能帮我在 r 中使用 parallel 来减少 Elbow 方法的时间？非常感谢。

Answer 1

您可以在 R 中使用库（并行）包。但是您必须考虑使用 clusterEvalQ()、clusterExport() 将变量和包导入您的环境。我认为您的代码如下所示：库（并行）

#

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
no_cores <- detectCores();
cl<-makeCluster(no_cores);
clusterEvalQ(cl, library(GMD));
clusterExport(cl, list("clustering.kmeans")); // add variables and functions to your enviroment
start.time <- Sys.time();
k.clusters <- parSapply(cl, 1, function(x) elbow.k(data.clustering)); // or parLapply - it returns list. 
end.time <- Sys.time();
cat('Time to find k using Elbow method is',(end.time - start.time),'seconds with k value:', k.clusters);
stopCluster(cl);

Answer 2

这是一个使用 k-means 创建肘部图的共享内存并行示例。

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")
}

这样使用：

parallel_elbow(100, iris[,-5])

比较文档术语矩阵与 2176 个文档的运行时间：

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

橙色正常，蓝色平行。

如何运行平行 Elbow 方法找到合适的 k-clusters

How to run parallel Elbow method to find appropriate k-clusters

parallel-processing

r

k-means

#

如何 运行 平行 Elbow 方法找到合适的 k-clusters

How to run parallel Elbow method to find appropriate k-clusters

parallel-processing

r

k-means

#

如何运行平行 Elbow 方法找到合适的 k-clusters