确定基于序列（距离）的群集的理想簇数

Question

我已经写了这些函数以用于群集基于序列的数据：

library(TraMineR)
library(cluster)

clustering <- function(data){
  data <- seqdef(data, left = "DEL", gaps = "DEL", right = "DEL")
  couts <- seqsubm(data, method = "CONSTANT")
  data.om <- seqdist(data, method = "OM", indel = 3, sm = couts)
  clusterward <- agnes(data.om, diss = TRUE, method = "ward")
  (clusterward)
}

rc <- clustering(rubinius_sequences)

cluster_cut <- function(data, clusterward, n_clusters, name_clusters){
  data <- seqdef(data, left = "DEL", gaps = "DEL", right = "DEL")
  cluster4 <- cutree(clusterward, k = n_clusters)
  cluster4 <- factor(cluster4, labels = c("Type 1", "Type 2", "Type 3", "Type 4"))
  (data[cluster4==name_clusters,])
}

rc1 <- cluster_cut(project_sequences, rc, 4, "Type 1")

. 但是，这里有任意分配的簇数。有些方法可以表明，通过一定数量的簇捕获的方差量（或一些类似的测量）开始达到一定数量的群集递增的递减点？我想象类似于a scree plot因子分析。

Answer 1

library(WeightedCluster)  
(agnesRange <- wcKMedRange(rubinius.dist, 2:10))
plot(agnesRange, stat = c("ASW", "HG", "PBC"), lwd = 5)

.

这将为查找理想数量的群集提供多个指标，以及图表。可以在此处找到有关索引的更多信息（在群集质量下）： http://mephisto.unige.ch/weightedcluster/