Topics Learnt Today:
Clustering methods for the project:
The Silhouette Scores, which serve as indicators of clustering quality, have been calculated for different clustering algorithms, each applied with five clusters. Detailed explanation of each method is below:
1: KMedoids Clustering (n_clusters=5):
- Silhouette Score: 0.37
- Interpretation: The score of 0.37 suggests moderate cohesion and separation between clusters. Points within clusters are reasonably well-matched to neighboring clusters. It indicates that there is some distinguishability between the clusters, but the separation is not exceptionally strong.
2: KMeans Clustering (n_clusters=5):
- Silhouette Score: 0.44
- Interpretation: The higher score of 0.44 indicates good cohesion and separation between clusters. Points within clusters are well-matched to neighboring clusters, signifying a more distinct and well-defined clustering compared to KMedoids. The clusters are relatively well-separated.
3: DBSCAN Clustering (eps=0.5, min_samples=5):
- Silhouette Score: -1
- Interpretation: The negative score of -1 is concerning. It suggests potential issues with the clustering quality, indicating that the DBSCAN algorithm may not be suitable for the given data and parameter settings. A negative silhouette score implies that points are inappropriately assigned to clusters, and the algorithm struggles to define meaningful clusters with the specified parameters.
In summary, the Silhouette Scores provide insights into the performance of different clustering algorithms. KMeans exhibits the highest score (0.44), indicating more distinct and well-separated clusters compared to KMedoids and DBSCAN. The negative score for DBSCAN suggests challenges in forming meaningful clusters with the specified parameters, highlighting potential issues in the clustering process for this algorithm in the given context.