kmeans can only take number of clusters or centers, but not both. And in the function, you constantly assigned something from the environment, which defeats the purpose of the function. Try something like this: set.seed(345)KmeansClustering<-function(Datapoints,ncluster=NULL,initialClusters=NULL...
The next step is I'm running the kmeans() function in R on the data set to find clusters, in testing I've found that 5 clusters is almost always a good choice via the "elbow method". The issue I'm having is that these clusters have distinct meanings/interpretations, so I want to ...
round <- 10 # 每次迭代10次,避免局部最优 rst <- sapply(K, function(i){ print(paste("K=",i)) mean(sapply(1:round,function(r){ print(paste("Round",r)) result <- My_kmeans(data, i) stats <- cluster.stats(dist(data), result$cluster) stats$avg.silwidth })) }) # 可以看到如...
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kmeans法(K均值法)是麦奎因提出的,这种算法的基本思想是将每一个样本分配给最靠近中心(均值)的类中,具体的算法至少包括以下三个步骤: 1.将所有的样品分成k个初始类; 2.通过欧氏距离将某个样品划入离中心最近的类中,并对获得样品与失去样品的类重新计算中心坐标; 3
kMeansAssignCluster <- function(x, centers) { minDist <- 10e10 centerIndex <- 0 for (center in 1:nrow(centers)) { dist <- dist(rbind(x, centers[center,])) if (dist < minDist) { minDist <- dist centerIndex <- center
R语言的自带函数为kmeans(),输入参数为数据和质心数量和最大迭代次数,默认为10次,具体可参考R语言的kmeans文档,我们在这里不多做赘述,接下来我们根据上面的算法核心来编写R语言代码: customKmeans<-function(dataset=NA,k=NA){ if(is.na(dataset) || is.na(k)){ ...
fdp<-function(X,G,p=rep(FALSE){ if(is.null(TstX)==TRUE) TstX<-X n<-nrow(TstX) belong<-matrix( plot(iris,col=cl 最受欢迎的见解 1.R语言k-Shape算法股票价格时间序列聚类 2.R语言基于温度对城市层次聚类、kmean聚类、主成分分析和Voronoi图 ...
fdp<-function(X,G,p=rep(FALSE){if(is.null(TstX)==TRUE) TstX<-X n<-nrow(TstX) belong<-matrix(plot(iris,col=cl 最受欢迎的见解 1.R语言k-Shape算法股票价格时间序列聚类 2.R语言基于温度对城市层次聚类、kmean聚类、主成分分析和Voronoi图 ...
1# k取2到8,评估K2K<-2:83round<-30# 每次迭代30次,避免局部最优4rst<-sapply(K,function(i){5print(paste("K=",i))6mean(sapply(1:round,function(r){7print(paste("Round",r))8result<-kmeans(norm.data, i)9stats<-cluster.stats(dist(norm.data), result$cluster)10stats$avg.silwidth11...