Skip to content

Latest commit

 

History

History
81 lines (59 loc) · 1.94 KB

README.md

File metadata and controls

81 lines (59 loc) · 1.94 KB
Raw

K-means-Clustering-on-HubwayTrips-Dataset

#####R Script File for K-means Clustering in R where normalization is required

getwd()

hubwaytrip = read.csv("HubwayTrips.csv")

str(hubwaytrip)

Normalization

install.packages("caret") library(caret) preproc = preProcess(hubwaytrip) preproc hubwaytripNorm = predict(preproc, hubwaytrip) hubwaytripNorm mean(hubwaytripNorm$Duration) sd(hubwaytripNorm$Duration) mean(hubwaytripNorm$Age) sd(hubwaytripNorm$Age) mean(hubwaytripNorm$Afternoon) sd(hubwaytripNorm$Afternoon) mean(hubwaytripNorm$Evening) sd(hubwaytripNorm$Evening) mean(hubwaytripNorm$Night) sd(hubwaytripNorm$Night) mean(hubwaytripNorm$Weekday) sd(hubwaytripNorm$Weekday) mean(hubwaytripNorm$Weekend) sd(hubwaytripNorm$Weekend) mean(hubwaytripNorm$Male) sd(hubwaytripNorm$Male) mean(hubwaytripNorm$Age) sd(hubwaytripNorm$Age)

K-means clustering with 10 clusters

set.seed(1234) KmeansClustering = kmeans(hubwaytripNorm, centers = 10)

Examination of results

table(KmeansClustering$cluster) tapply(hubwaytrip$Duration, KmeansClustering$cluster, mean) tapply(hubwaytrip$Morning, KmeansClustering$cluster, mean) tapply(hubwaytrip$Afternoon, KmeansClustering$cluster, mean) tapply(hubwaytrip$Evening, KmeansClustering$cluster, mean) tapply(hubwaytrip$Night, KmeansClustering$cluster, mean) tapply(hubwaytrip$Weekday, KmeansClustering$cluster, mean) tapply(hubwaytrip$Weekend, KmeansClustering$cluster, mean) tapply(hubwaytrip$Male,KmeansClustering$cluster, mean) tapply(hubwaytrip$Age, KmeansClustering$cluster, mean)

Visualiseing the clusters

install.packages('ggpubr') library(ggpubr) install.packages('factoextra') library(factoextra) fviz_cluster(KmeansClustering, data = hubwaytrip, geom = "point", ellipse.type = "convex", ggtheme = theme_bw() )

fviz_cluster(KmeansClustering, data = hubwaytrip, geom = "point", ellipse.type = "euclid", ggtheme = theme_bw() )