shinykmeans聚类结果可视化.pptx

shiny -kmeans聚类结果可视化 shiny -kmeans聚类结果可视化kmeans K均值聚类算法是先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。 shiny -kmeans聚类结果可视化kmeans结果可视化library(shiny)ui = pageWithSidebar( headerPanel(Iris k-means clustering), sidebarPanel( # 侧边栏 selectInput(xcol, X Variable, names(iris)), # 选择框 selectInput(ycol, Y Variable, names(iris), selected=names(iris)[[2]])， numericInput(clusters, Cluster count, 3,min = 1,max = 9) #要标记的个数 ), mainPanel(plotOutput(plot1))) server = function(input, output, session) { selectedData - reactive({ # 数据重组 iris[, c(input$xcol, input$ycol)] }) clusters - reactive({ # 数据重组 kmeans(selectedData(), input$clusters) }) output$plot1 - renderPlot({ palette(c(#E41A1C, #377EB8, #4DAF4A, #984EA3, #FF7F00, #FFFF33, #A65628, #F781BF, #999999)) shiny -kmeans聚类结果可视化kmeans结果可视化 par(mar = c(5.1, 4.1, 0, 1)) plot(selectedData(), col = clusters()$cluster, pch = 20, cex = 3) points(clusters()$centers, pch = 4, cex = 4, lwd = 4) })}shiny -kmeans聚类结果可视化kmeans结果可视化 shiny -kmeans聚类结果可视化kmeans结果可视化 library(shiny)library(dplyr)ui = shinyUI( pageWithSidebar( headerPanel(kmeans), sidebarPanel( numericInput(n, Number of obs, 500 ,min=200 ,max=1000), selectInput(type, Select a clust approach:,c(kmeans),kmeans) # 这里使用选择框，便于后期增加其他聚类函数 ),mainPanel(plotOutput(plot)) ))shiny -kmeans聚类结果可视化kmeans结果可视化 server = function(input, output,session) { set.seed(500) selectedData - reactive({ # 数据重组 rbind( data_frame(x = rnorm(input$n), y = rnorm(input$n)), data_frame(r = rnorm(input$n, 5, .25), theta = runif(input$n, 0, 2 * pi), x = r * cos(theta), y = r * sin(theta)) %% dplyr::select(x, y) ) }) output$plot - renderPlot({ switch(input$type, # 选择不同的聚类函数，本例仅一个 kmeans =( plot(selectedData(), col = kmeans(selectedData(),2)$cluster, pch = 20, cex = 1)