• K-Means聚類算法

# 設置工作空間 # 把“數據及程序”文件夾拷貝到F盤下，再用setwd設置工作空間 setwd("E:\\R_workspace\\R語言數據分析與挖掘實戰\\chp5")# 讀入數據 Data <- read.csv("./data/consumption_data.csv", header = TRUE)[, 2:4]View(Data) fix(Data)# center=3 為3個聚類中心，默認為 歐式距離 km <- kmeans(Data, center = 3) print(km)# 每組分類所占的比例 km$size / sum(km$size)# 數據分組 aaa <- data.frame(Data, km$cluster) Data1 <- Data[which(aaa$km.cluster == 1), ] Data2 <- Data[which(aaa$km.cluster == 2), ] Data3 <- Data[which(aaa$km.cluster == 3), ]# 類別1的情況 summary(Data1) View(Data1)# 客戶分群“1”的概率密度函數圖 par(mfrow = c(1,3)) plot(density(Data1[, 1]), col = "red", main = "R") plot(density(Data1[, 2]), col = "red", main = "F") plot(density(Data1[, 3]), col = "red", main = "M")# 客戶分群“2”的概率密度函數圖 par(mfrow = c(1, 3)) plot(density(Data2[, 1]), col="red", main = "R") plot(density(Data2[, 2]), col="red", main = "F") plot(density(Data2[, 3]), col="red", main = "M")# 客戶分群“3”的概率密度函數圖 par(mfrow = c(1, 3)) plot(density(Data3[, 1]), col="red", main = "R") plot(density(Data3[, 2]), col="red", main = "F") plot(density(Data3[, 3]), col="red", main = "M")

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