This is an app done as part of the Developing Data Products online course on Coursera.

This app provides an interface for a user to run K-means clustering on the iris dataset

A slider input is provided for the user to control the number of clusters.

By default, the K-means clustering function does not print out a user friendly output:

kmeans_mdl <- kmeans(iris[, 3:4], 3)
kmeans_mdl

## K-means clustering with 3 clusters of sizes 52, 48, 50
## 
## Cluster means:
##   Petal.Length Petal.Width
## 1     4.269231    1.342308
## 2     5.595833    2.037500
## 3     1.462000    0.246000
## 
## Clustering vector:
##   [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##  [36] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [71] 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
## [106] 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2
## [141] 2 2 2 2 2 2 2 2 2 2
## 
## Within cluster sum of squares by cluster:
## [1] 13.05769 16.29167  2.02200
##  (between_SS / total_SS =  94.3 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"    
## [5] "tot.withinss" "betweenss"    "size"         "iter"        
## [9] "ifault"

However, with the app, it produces a nice plot which allows the user to visualise the results of the clustering: