Silhouette Score is a metric used to measure the quality of a cluster. It is a measure of how close each point in one cluster is to points in the neighboring clusters.
Silhouette Score ranges from -1 to 1, where a score closer to 1 indicates that the data points in the cluster are much closer to other data points in the same cluster than those in other clusters. Conversely, a score closer to -1 indicates that the data points may have been assigned to the wrong cluster.
To calculate Silhouette Score, two measures must be taken into account:
- a: The mean distance between a data point and all other points in its own cluster (the “intra-cluster” distance)
- b: The mean distance between a data point and all other points in the nearest neighboring cluster (the “nearest-cluster” distance)
The Silhouette Score for each data point is then calculated as follows:
(b – a) / max(a, b)
Finally, an overall Silhouette score for all data points can be calculated by taking the mean of all individual Silhouette scores.
Silhouette Score is an effective way of evaluating clustering algorithms since it takes both intra-cluster and inter-cluster distances into account. It helps developers identify which clustering algorithm provides better results by comparing different algorithms with different parameters. This information can then be used to optimize clusters and improve performance.
Conclusion:
In conclusion, Silhouette Score is an important metric for evaluating clustering algorithms. It provides an indication of how well each data point has been assigned to its respective cluster by comparing intra-cluster and inter-cluster distances. By using this metric, developers can identify which clustering algorithm produces better results and optimize clusters accordingly.