You will now walk through a practical example of applying **K-means clustering**. To do this, you'll use a **dummy dataset**. Dummy datasets are artificially generated datasets that are often used for demonstration and learning purposes. They allow us to **control the characteristics of the data** and clearly observe how algorithms like K-means perform.

## Dummy Dataset

For this demonstration, we will create a dummy dataset using the `make_blobs()` function. This function is excellent for generating clusters of data points in a **visually clear** and **controllable** way. We will generate data with the following characteristics: 

-  **Number of samples**: we will create a dataset with `300` data points; 

- **Number of centers**: we will set the number of true clusters to `4`. This means the dummy data is designed to have four distinct groups; 

- **Cluster standard deviation**: we will control the spread of data points within each cluster, setting it to `0.60` for relatively compact clusters; 

- **Random state**: we will use a fixed `random_state` for reproducibility, ensuring that the data generation is consistent each time you run the code.     

```python
X, y_true = make_blobs(n_samples=300,
                       centers=4,
                       cluster_std=0.60,
                       random_state=0)
```

## K-Means Implementation

With this dummy data created, we will then apply the **K-means algorithm**. We will explore how K-means attempts to **partition this data into clusters** based on the principles you learned in previous chapters. 

K-means can be initialized and trained as follows in Python:

```python
kmeans = KMeans(n_clusters=k, random_state=42) 
kmeans.fit(X)
``` 

To determine the **optimal number of clusters** for this data, we will employ the methods discussed in the previous chapters: 

- **WSS method**: we will calculate the Within-Sum-of-Squares for different values of K and analyze the elbow plot to identify a potential optimal K; 

- **Silhouette score method**: we will compute the Silhouette Score for different values of K and examine the Silhouette plot and average Silhouette scores to find the K that maximizes cluster quality. 

Finally, **visualizations** will play a crucial role in our implementation. We will visualize: 

- The dummy data itself, to see the **inherent cluster structure**;     

- The **WSS plot**, to identify the elbow point; 

- The **silhouette plot**, to assess cluster quality for different K values; 

- The **final K-means clusters** overlaid on the dummy data, to visually verify the clustering results and the chosen optimal K. 

Le clustering est l'une des techniques fondamentales de l'apprentissage automatique qui vous permet de résoudre de nombreux problèmes complexes dans la vie réelle : segmentation de marché, détection d'anomalies, réduction de la dimensionnalité, révélation de motifs cachés, etc.

Dive into the fundamentals of clustering and discover how it differs from classification. Explore essential algorithms, tools, and libraries that power this unsupervised learning technique to uncover hidden patterns in data.

Gain a solid understanding of key preprocessing techniques that ensure effective clustering. Learn how to handle missing values, encode categorical features, normalize data, and choose appropriate distance measures and linkages to boost clustering accuracy.

Master the skills needed to apply K-Means clustering effectively. Learn how the algorithm works, determine the optimal number of clusters, and gain hands-on experience by implementing K-Means on both synthetic and real-world datasets.

Explore the essentials of hierarchical clustering and learn how to group data into meaningful clusters using dendrograms. Build confidence in identifying the optimal number of clusters and implementing the technique on both synthetic and real-world datasets.

Discover how DBSCAN excels at detecting clusters of varying shapes and handling noise in data. Learn the mechanics behind this density-based algorithm, how to assign points to clusters, and apply it to both synthetic and real datasets with confidence.

Gain a solid understanding of Gaussian Mixture Models and how they use probability to model complex cluster shapes. Learn the principles of Gaussian distribution, explore how GMMs work, and build confidence by applying them to both dummy and real-world data.

Analyse de Cluster

Implementing on Dummy Dataset