GITNUX MARKETDATA REPORT 2023
Must-Know Clustering Metrics
Highlights: The Most Important Clustering Metrics
- 1. Adjusted Rand Index (ARI)
- 2. Mutual Information (MI)
- 3. Homogeneity Score
- 4. Completeness Score
- 5. V-Measure Score
- 6. Fowlkes-Mallows Index (FMI)
- 7. Silhouette Coefficient
- 8. Calinski-Harabasz Index (CHI)
- 9. Davies-Bouldin Index (DBI)
- 10. Dunn Index
- 12. Gap Statistic
Table of Contents
Clustering Metrics: Our Guide
Discover the critical insights you need to optimize your business operations with our newly updated report on must-know clustering metrics. Gain an in-depth understanding of the role these analytics play in improving your data mining strategies and making data-driven decisions. Stay ahead of the curve by learning the latest trends, technologies, and best practices in this crucial aspect of modern information management.
Adjusted Rand Index
Measures clustering similarity adjusted for chance, ranging from -1 to 1, with higher values indicating better clustering, considering true and false positives/negatives.
Measures shared information between two clusterings, indicating agreement with true labels. Higher MI scores indicate better clustering.
Measures cluster purity, indicating how well each cluster contains members of a single class. Higher homogeneity scores (0 to 1) indicate purer clusters.
Measures the extent to which all members of a class belong to the same cluster. A higher completeness score (0 to 1) indicates better agreement between clustering and true labels.
This is the harmonic mean of Homogeneity and Completeness scores. A higher V-measure score (range: 0 to 1) signifies better clustering.
Computes clustering similarity using the geometric mean of precision and recall. Scores range from 0 to 1, with higher values indicating better clustering quality.
Measures clustering cohesion and separation through average silhouette scores for each sample. Scores range from -1 to 1, with higher values indicating better clustering quality.
Assesses the ratio of between-cluster to within-cluster dispersion, with higher values indicating better-defined clusters.
Calculates the ratio of within-cluster distances to between- cluster distances, where a lower DBI score suggests better clustering quality.
This metric measures the ratio of the minimum inter-cluster distance to the maximum intra-cluster distance. A higher Dunn Index implies better-separated and compact clusters.
This metric aims to minimize inertia by summing squared distances between samples and their cluster means, promoting tighter clusters.
It selects the number of clusters as the smallest k for which the gap statistic is within one standard error of the highest value, with a larger gap statistic indicating better clustering.
Frequently Asked Questions
What are clustering metrics, and why are they important?
What are some common clustering metrics?
How is the silhouette coefficient used in evaluating clusters?
How does the adjusted Rand index (ARI) work?
What is the difference between internal and external clustering metrics?
How we write these articles
We have not conducted any studies ourselves. Our article provides a summary of all the statistics and studies available at the time of writing. We are solely presenting a summary, not expressing our own opinion. We have collected all statistics within our internal database. In some cases, we use Artificial Intelligence for formulating the statistics. The articles are updated regularly. See our Editorial Guidelines.