Clustering is a technique in machine learning and data analysis that groups similar data points based on their features without requiring labeled data.
The goal of clustering is to create distinct clusters where data points within the same cluster are more similar to each other than to those in other clusters.
Common clustering algorithms include K-Means, Hierarchical Clustering, and DBSCAN, each with its approach to measuring similarity between data points.
Various evaluation methods and metrics are used to assess clustering results, including Silhouette Score, Davies-Bouldin Index, and Dunn Index for internal evaluation, as well as metrics like Adjusted Rand Index, Normalized Mutual Information, and Fowlkes-Mallows Index for external evaluation if ground truth is available.
Visual inspection techniques, such as scatter plots, t-SNE, and PCA, can help provide insights into the quality of clusters.
In some cases, domain-specific evaluation may be necessary to assess the clusters’ utility for solving real-world problems.
The choice of evaluation metrics should consider the data’s characteristics and the analysis goals, often involving a combination of metrics and visual inspection for a comprehensive assessment.