Clustering Based On Geographical Locations

Loading the Data Libraries

library(data.table)
library(ggplot2)

Loading the datafile containing various places to be clustered

locations <- fread("C:/Documents/Data Science/Cluster Analysis in Data Mining/PAW2/Places.txt",header = FALSE,na.strings = "NA",stringsAsFactors = FALSE, skip = 0)
dim(locations)

## [1] 300   2

Using K-Means Clustering methodology to create 3 different clusters

km <- kmeans(locations,centers = 3)
km

## K-means clustering with 3 clusters of sizes 100, 100, 100
## 
## Cluster means:
##           V1       V2
## 1 -112.07161 33.46049
## 2  -80.84423 35.21710
## 3  -80.52837 43.47625
## 
## Clustering vector:
##   [1] 1 1 1 1 3 3 2 1 1 2 1 2 3 1 2 2 2 1 2 3 3 3 3 2 1 3 1 3 1 1 3 2 1 1 1
##  [36] 2 3 1 3 2 2 2 2 2 2 3 1 2 1 1 3 2 1 2 3 2 1 2 3 2 1 3 2 1 3 2 2 1 3 2
##  [71] 3 1 2 3 1 1 1 2 3 1 1 2 1 3 1 1 3 1 3 1 3 3 1 3 3 3 2 2 1 1 1 3 1 1 2
## [106] 2 1 3 1 1 3 2 1 3 1 1 3 1 3 3 2 2 1 3 2 3 2 3 2 3 2 3 2 1 1 3 3 1 1 3
## [141] 2 2 1 3 2 2 2 1 2 2 3 2 2 3 3 3 3 3 2 2 3 1 3 1 2 3 3 3 3 3 3 1 1 2 2
## [176] 1 3 2 2 3 1 3 2 2 3 2 2 3 1 3 2 2 1 1 1 2 1 1 2 1 3 1 2 1 3 2 1 3 2 2
## [211] 3 2 2 1 3 3 1 3 1 3 1 3 3 3 2 2 2 1 2 1 1 2 2 3 3 2 1 2 1 2 1 1 2 2 2
## [246] 2 2 2 2 1 2 2 3 3 3 3 2 1 2 1 3 1 2 3 3 1 3 1 3 1 3 2 1 2 3 3 1 1 3 3
## [281] 2 2 2 2 3 3 2 3 3 1 3 1 2 1 3 1 1 1 3 1
## 
## Within cluster sum of squares by cluster:
## [1] 0.03046838 0.20364907 0.02345053
##  (between_SS / total_SS = 100.0 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"    
## [5] "tot.withinss" "betweenss"    "size"         "iter"        
## [9] "ifault"

Assigning the cluster numbers to each point

a <- km$cluster-1
df <- cbind("generated_uid3" = sprintf("%01d", 1:nrow(locations)-1), a)
fix(df)

Creating a text file that displays assignment of cluster number to each data point

write.table(df,file = "Cluster.txt",sep = , row.names = FALSE, col.names = FALSE, quote = FALSE)