5 Ways for Deciding Number of Clusters in a Clustering Model | Machine Learning | Python
Deciding the optimal number of clusters is a critical step in building an unsupervised clustering model. In this tutorial, we will talk about five ways to decide the number of clusters for a clustering model in Python. You will learn:
👉 How to use the elbow method on within cluster sum of squares to decide the number of clusters?
👉 How to use the Silhouette score to decide the number of clusters?
👉 How to use the hierarchical graph to find the optimal number of clusters visually?
👉 How to use AIC and BIC for choosing the number of clusters?
👉 How to use gap statistics to decide the number of clusters?
⏰ Timecodes ⏰
0:00 - Intro
0:32 - Step 1: Import Libraries
1:37 - Step 2: Read Data
2:50 - Step 3: Dimensionality Reduction and Visualization
3:40 - Step 4: Deciding Number of Clusters Using Elbow Method (Method 1)
4:20 - Step 5: Deciding Number of Clusters Using Silhouette Score (Method 2)
5:15 - Step 6: Deciding Number of Clusters Using Hierarchical Graph (Method 3)
5:41 - Step 7: Deciding Number of Clusters Using AIC and BIC from GMM (Method 4)
6:28 - Step 8: Deciding Number of Clusters Using Gap Statistics (Method 5)
7:11 - Step 9: Which method to use for the optimal number of clusters?
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