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Clustering Models Guidebook

🎯 Purpose

This guidebook introduces foundational unsupervised clustering models and their real-world applications. It serves as the entry point to the clustering section of the Analyst Toolkit Vault and is designed for analysts exploring structure in unlabeled data.

🧠 1. What is Clustering?

Clustering is the process of grouping similar observations together based on feature similarity. It is:

  • Unsupervised: no labeled target variable
  • Exploratory: often part of EDA or segmentation
  • Flexible: used in customer segmentation, anomaly detection, pattern discovery

📦 2. Common Clustering Algorithms

Model Best For Notes
K-Means Well-separated, spherical clusters Fast, needs k, sensitive to scale
Hierarchical Clustering Small datasets, dendrogram needs Good for nested patterns
DBSCAN Arbitrary shape, noise handling Density-based, auto-detects clusters
Gaussian Mixture (GMM) Overlapping clusters Probabilistic, soft clustering
HDBSCAN Complex, hierarchical structures Robust alternative to DBSCAN
Spectral Clustering Graph-based clusters Useful for non-convex shapes
K-Medoids Robust to outliers Like K-Means but more stable

⚙️ 3. Preprocessing for Clustering

Step Why It Matters
Feature Scaling K-Means, GMM are distance-based
Dimensionality Reduction PCA/UMAP for visualization or speed
Encoding Categoricals Required for mixed-type data
Outlier Removal Affects DBSCAN, K-Means boundaries 
from sklearn.preprocessing import StandardScaler
X_scaled = StandardScaler().fit_transform(X)

📈 4. K-Means Clustering

How It Works

  • Assigns k cluster centroids
  • Iteratively minimizes within-cluster variance
from sklearn.cluster import KMeans
model = KMeans(n_clusters=3)
model.fit(X_scaled)
labels = model.labels_

Strengths:

  • Fast, scalable
  • Interpretability of centroids

Limitations:

  • Assumes spherical clusters
  • Must choose k
  • Sensitive to scale & initialization

🌲 5. Hierarchical Clustering

How It Works

  • Computes all pairwise distances
  • Iteratively merges closest clusters (linkage)
from scipy.cluster.hierarchy import linkage, dendrogram
linkage_matrix = linkage(X_scaled, method='ward')
dendrogram(linkage_matrix)

Use When:

  • You need a dendrogram
  • Want to explore cluster granularity at multiple levels

🌊 6. DBSCAN (Density-Based)

How It Works

  • Forms clusters by detecting dense regions
  • Labels noise and outliers
from sklearn.cluster import DBSCAN
model = DBSCAN(eps=0.3, min_samples=5)
labels = model.fit_predict(X_scaled)

Strengths:

  • Detects arbitrary shapes
  • Handles noise automatically

Limitations:

  • Needs parameter tuning (eps, min_samples)
  • Struggles in varying density

🎲 7. Gaussian Mixture Models (GMM)

How It Works

  • Fits multiple Gaussian distributions
  • Each point gets a probability of belonging to each cluster
from sklearn.mixture import GaussianMixture
model = GaussianMixture(n_components=3)
labels = model.fit_predict(X_scaled)

Use When:

  • You expect soft cluster boundaries
  • Data is more overlapping or elliptical

📊 8. Comparing Cluster Results

Tool Purpose
Silhouette Score Measures cohesion vs separation
Calinski-Harabasz Ratio of between/within dispersion
Davies-Bouldin Lower = better separation/compact
Visual Inspection Dimensionality-reduced plots (PCA, UMAP) 
from sklearn.metrics import silhouette_score
score = silhouette_score(X_scaled, labels)

📌 9. Tips for Analysts

  • Always scale your data unless using tree-based distances
  • Use PCA or UMAP for 2D cluster plotting
  • Don’t assume clustering is “correct” — it’s often exploratory
  • Validate with multiple metrics AND domain context

📅 Next Steps

  • [ ] Build advanced clustering guide (validation, deep methods)
  • [ ] Add visual clustering guide (PCA, UMAP, silhouette visual)
  • [ ] Create checklist + decision card for clustering model choice