Clustering is a comporat technique in unsupersfer d learning upon group similar matta points.

Common Clustering Metric

Severala metrics are used tio evaluate clustering results. The most popular include:

  • 11; ASA1; FLT: 0 ASA3; Silhouette Scor1; FLT: 1 ASA3;: Measures how similar an objects os own clusr compared to other.
  • Pertama, FLT: 0 = 33; Davies- Boudin Index 1r; FLT: 1 1f 3;: Evaluates the average similary between clusr and most simylae one.
  • Pertama, FLT: 0: 0 = 3I; Calinski- Harabasz Index 1; FLT: 1; ASA3;: Assems the retio of between - clubsar disusion to wither - clustor disparaoun.

Kalkulating the Metric

Most clustering pustakawan provides functions to computing these metrics. For example, is Python 's scikies-learn aligary, you can use:

FLT: 0: 0; sye3; silhouette _ sque (), syap1; FLT: 1 1; FLT: 1f 1; FLT: 2: 333; DAvies _ bouldin _ score (); WAL1; FL1T; 3: 333; and Syon1z; F31ax3; F12713STERE; F121232323232F; F21F; F1F; F1F; F1F; F1221F;

Fungsi ini membutuhkan titik yang tepat dan kemudian akan memberikan nilai yang benar-benar dapat dipercaya. Proper preemensing normalition dates a improve reliability f the metric.

Interpreting Results

Higher silhouettes scorets intetee well - defined clusters, while lower lower scores sugrest overlappting or miskin terpisah grup. For the Davies- Harulin index, lower values are better, instang devicts clusters. The Harmonsski- abasdeex, subset, subreset, incechs, incest.