Evaluating Clustering Performance
To evaluate the quality of the clustering results, one must evaluate the clustering performance. Calinski-Harabasz index, Davies-Bouldin index, and silhouette score are examples of common evaluation measures.
Metrics for evaluating clustering results
- The compactness and separation of clusters are measured by the silhouette score.
- The Davies-Bouldin Index measures the average similarity, normalized by the spread of the cluster, between each cluster and its most similar cluster.
- Calculates the ratio of within-cluster dispersion to between-cluster dispersion using the Calinski-Harabasz index.
PyTorch for Unsupervised Clustering
The aim of unsupervised clustering, a fundamental machine learning problem, is to divide data into groups or clusters based on resemblance or some underlying structure. One well-liked deep learning framework for unsupervised clustering problems is PyTorch.
Table of Content
- What is Unsupervised Clustering?
- K-means Clustering
- Hierarchical Clustering
- DBSCAN Clustering
- Evaluating Clustering Performance
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