Machine Learning clustering is an Unsupervised Learning method that groups similar data points together based on their features, forming clusters of related items without prior labels.
Notes
Info
Clustering has applications in many fields including computer vision, biology, marketing, and social network analysis.
Clustering algorithms have helped uncover hidden patterns in various fields:
- Marketing: Grouping customers based on purchasing behavior for targeted campaigns
- Biology: Analyzing gene expression data to identify new disease subtypes or understand regulatory mechanisms
- Astronomy: Classifying galaxies based on their morphology and properties
TakeAways
- 📌 Main Point:
- Identifies hidden patterns & structures without prior labels
- Used for customer segmentation, image segmentation, anomaly detection & dimensionality reduction.
- 💡 Important Information:
- Common algorithms: K-means, Hierarchical, DBSCAN.
- Evaluation metrics: Silhouette score, Calinski-Harabasz index, Davies-Bouldin index.
- Challenges: Number of clusters, Outliers, Noise.
- 🔍 Key Data:
- K-means: Most popular algorithm; assumes spherical clusters
- hierarchical clustering: Offers different linkage criteria & distance metrics
Process
- 🧑🏫 Choose appropriate clustering algorithm based on dataset & requirements
- 📁 Preprocess data: normalize, scale, handle missing values.
- 🎛️ Tune hyperparameters: e.g., K in k-means, ε in DBSCAN, using techniques like Cross-Validation.
- 🎯 Evaluate Model Performance using evaluation metrics
- 🔄 Validate clusters & refine algorithm as needed
Thoughts
- ❓ Consideration: Different algorithms have different assumptions about data distribution; choose wisely.
- 🌱 Challenge: Clustering is sensitive to outliers & noise. Preprocessing may help improve results.
- 🎯 Goal: Achieve the most compact and separable clusters, minimizing intra-cluster distance while maximizing inter-cluster distance.