Clustering Techniques

Definitions

• Clustering: The process of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.
• Centroid: A central point that represents a cluster.
• Distance Metric: A method to measure the distance or similarity between data points (e.g., Euclidean distance, Manhattan distance).

Common Clustering Techniques

1. K-Means Clustering: A partitioning method that divides data into K clusters, where each data point belongs to the cluster with the nearest mean.
2. Hierarchical Clustering: Builds a tree of clusters using either agglomerative (bottom-up) or divisive (top-down) approaches.
3. DBSCAN: Density-Based Spatial Clustering of Applications with Noise, groups together points that are closely packed together, marking points in low-density regions as outliers.
4. Gaussian Mixture Models: A probabilistic model that assumes all data points are generated from a mixture of a finite number of Gaussian distributions.

Clustering Workflow

graph TD;
    A[Start] --> B[Data Preparation]
    B --> C[Choose Clustering Algorithm]
    C --> D[Train Model]
    D --> E[Evaluate Clusters]
    E --> F[Visualize Results]
    F --> G[End]
            

Key Recommendations

• Normalize your data before clustering to avoid skewed results.
• Choose the right number of clusters using methods like the Elbow method or Silhouette score.
• Visualize clusters using techniques like PCA or t-SNE to understand data distribution better.

K-Means Clustering Example

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

# Generating synthetic data
X = np.random.rand(100, 2)

# Applying K-Means
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
labels = kmeans.labels_

# Plotting results
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red')
plt.title('K-Means Clustering')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.show()