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我编写了这些函数来对基于序列的数据进行聚类：

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")

然而，这里集群的数量是任意分配的。有什么方法可以证明一定数量的集群捕获的方差量（或某种类似的度量）在一定数量的集群上开始达到收益递减点？我正在想象类似于因子分析中的碎石图。

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1 回答 1

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

这将为查找理想的集群数量以及图形提供多个索引。关于指数的更多信息可以在这里找到（在集群质量下）： http: //mephisto.unige.ch/weightedcluster/

于 2014-02-28T02:04:32.350 回答