Clustering-Techniques-on-Synthetic-Real-World-Data

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

1. Used K-means and hierarchical clustering methods to generate clusters
2. Evaluated the performance of the clustering algorithm using external validation metrics like Jaccard index
3. Plotted the data points for each dataset and colored them according to the original class using ggplot2 package
4. Plotted the data points for each dataset and colored them according to the class allocated by the clustering algorithm
5. Used Internal validation metrics like Silhouette and Dunn index to report the cluster quality USING WCSS ( Within-Cluster Sum of Square )