World Indicators Clustering

Applied K-means and hierarchical clustering methods on World Indicators dataset

The objective of this project is to implement different clustering methods to synthetic and real-world data and validate using external and internal validation techniques

Synthetic Dataset

  • Applied clustering methods (K-means and hierarchical clustering) to generate clustering from 8 datasets (“Data1.csv” to “Data8.csv”)
  • Evaluated the performacne of the clustering algorithm using external validation.
  • Plot (2D or 3D) the data points for each dataset and color them according to the original class
  • Plot (2D or 3D) the data points for each dataset and color them according to the class allocated by the clustering algorithm

Real-word data (World Indicators dataset)

  • Applied K-means and hierarchical clustering methods to group similar countries together
  • Used Internal validation metrics to report the cluster quality
  • Conducted experiments to find appropriate numbers of clusters by using Silhouette analysis and Elbow method

Jupiter Notebook Link