Silhouette measures are a useful way to measure the accuracy of a cluster analysis. It is used to measure how closely related objects in a cluster are to one another, and how distinct they are from objects in other clusters.
The Silhouette measure is based on two different distances. The first distance is the distance between an object and all other objects in its own cluster (the intra-cluster distance). The second distance is the distance between an object and all objects in other clusters (the inter-cluster distance).
The Silhouette measure then compares these two distances by calculating the ratio of the intra-cluster to the inter-cluster distances, which results in a value between -1 and 1. A higher ratio indicates that a data point is better matched with its own cluster than with any other clusters, whereas lower ratios indicate that it may be better matched with another cluster.
Calculation
The calculation of the Silhouette measure requires computing two different metrics for each data point:
- a) The average intra-cluster distances;
- b) The minimum inter-cluster distances.
These metrics are then used to calculate the Silhouette measure for each data point. The value of the Silhouette measure ranges from -1 to 1, where values close to 1 indicate that a data point is well matched within its own cluster, and values close to -1 indicate that it may be more appropriately matched with another cluster.
Uses
Silhouette measures can be used for a variety of tasks such as identifying outliers in a dataset or selecting optimal number of clusters from an unsupervised clustering algorithm. In addition, it can also be used for assessing the quality of clustering algorithms by computing the average Silhouette score over all data points.
Conclusion: What Does Silhouette Measure? Silhouette measures are used to assess how accurately objects in a dataset have been clustered into different groups by comparing their intra-cluster and inter-cluster distances. It provides a metric ranging from -1 to 1 which indicates how well an object has been assigned to its own cluster, with higher values indicating better assignment accuracy.
10 Related Question Answers Found
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