Kmeans Auto Data for Shinyapps.io

This app utilizes auto mpg data from UC Irvine, available at https://archive.ics.uci.edu/ml/datasets/Auto+MPG

The original dataset has been clenaed for missing values, but in all other respects is unaltered

Using Shiny, we can observer trends and relationships between the variables. In this app, we can calculate the kmeans for clusters in the data, varying from 1-9.

Below is an example of the dataset in use.

##   X mpg cylinders displacement horsepower weight acceleration model.year
## 1 1  18         8          307      130.0   3504         12.0         70
## 2 2  15         8          350      165.0   3693         11.5         70
## 3 3  18         8          318      150.0   3436         11.0         70
## 4 4  16         8          304      150.0   3433         12.0         70
## 5 5  17         8          302      140.0   3449         10.5         70
## 6 6  15         8          429      198.0   4341         10.0         70
##   origin                  car.name
## 1      1 chevrolet chevelle malibu
## 2      1         buick skylark 320
## 3      1        plymouth satellite
## 4      1             amc rebel sst
## 5      1               ford torino
## 6      1          ford galaxie 500

k-means clustering aims to partition our observations into a defined number of clusters, in which each observation belongs to the cluster with the nearest mean.

The center-point of the cluster then represents the mean of that partition.

A simple plot ilustrates the relationship between mpg and vehicle weight, however this does not lend itself to the type of analysis where we could group the data into categories.

On the other hand, a kmeans plot of the data can allow us to visualize where breaks in the samples occur.

Additionally, our shiny app can allow comparison of any two variables in the data set, and visualization of a number of different k-means buckets or partitions.