Solving clustering problem for iris data set with R-Shiny application

Using R we can easily solve different clustering problems. There are different clustering algorithms and it is essentially to try different algorithms before stating the results. As an example the R Shiny apllication was built, that solves clustering problem for the iris data set, using the following algorithms:

• K-means clustering algorithm
• Model-based clustering algorithm using Gaussian distributions
• K-medoids clustering

• R Shiny application: https://mykytazharov.shinyapps.io/KMeansVisualisation/
• Git-hub repo incluiding ui.R and server.R for the application and presnetation: https://github.com/mykytazharov/RShinyApplication

In the application there are several inpus, which a user can change.

• In the first dropdown menu a user can choose a clustering method to be used.
• Using a slider, a user can select a number of desired clusters.
• Using two dropdown menus at the end, a user can select 2 variables that will describe the data points.

After selecting desired inputs, a user can observe the visual result of solving a clustering problem on the plot. For example, for K-means clustering and 3 clusters for variables Sepal.Length and Sepal.Width a user will see the following output: