---

name: ds-clustering compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Implements clustering algorithms including K-means, hierarchical clustering DBSCAN, Gaussian mixture models, and spectral clustering"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-anomaly-detection, ds-association-rules, ds-community-detection ds-dimensionality-reduction ds-dimensionality-reduction role: implementation scope: implementation triggers: clustering, k-means, hierarchical clustering, DBSCAN, mixture models how do I cluster data archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

---

Clustering

Comprehensive guide to clustering in machine learning and data science workflows.

When to Use This Skill

• Solving real-world unsupervised learning problems
• Building machine learning pipelines with clustering
• Implementing best practices for clustering
• Optimizing model performance using clustering techniques
• Learning industry-standard approaches to clustering

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require clustering rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Clustering is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Clustering

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
from typing import Dict, Any

def basic_clustering_workflow(
    n_samples: int = 300, 
    n_clusters: int = 3
) -> Dict[str, Any]:
    """
    Demonstrates a basic clustering workflow with validation, scaling, 
    and evaluation metrics. Follows PEP 8 and scikit-learn API standards.
    """
    if n_samples < n_clusters:
        raise ValueError("Number of samples must exceed number of clusters")
        
    X, y_true = make_blobs(n_samples=n_samples, centers=n_clusters, random_state=42)
    
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    
    kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
    cluster_labels = kmeans.fit_predict(X_scaled)
    
    silhouette_avg = silhouette_score(X_scaled, cluster_labels)
    inertia = kmeans.inertia_
    
    return {
        "labels": cluster_labels
        "silhouette_score": float(silhouette_avg)
        "inertia": float(inertia)
        "centers": kmeans.cluster_centers_
    }

Pattern 2: Production-Ready Clustering

import logging
import pandas as pd
import numpy as np
from typing import Any, Dict, Literal
from sklearn.cluster import KMeans, DBSCAN
from sklearn.preprocessing import StandardScaler
import warnings

logger = logging.getLogger(__name__)

class ProductionClustering:
    """Production-ready clustering implementation with validation and metrics."""
    
    def __init__(self, algorithm: Literal["kmeans", "dbscan"] = "kmeans", **kwargs: Any):
        self.algorithm = algorithm
        self.kwargs = kwargs
        self.scaler = StandardScaler()
        self.model = None
        
    def _validate_input(self, data: pd.DataFrame) -> pd.DataFrame:
        if data.empty:
            raise ValueError("Input DataFrame cannot be empty")
        if not np.isfinite(data.values).all():
            raise ValueError("Input data contains non-finite values")
        return data.dropna()
        
    def execute(self, data: pd.DataFrame) -> Dict[str, Any]:
        try:
            clean_data = self._validate_input(data)
            X_scaled = self.scaler.fit_transform(clean_data)
            
            if self.algorithm == "kmeans":
                self.model = KMeans(random_state=42, n_init=10, **self.kwargs)
            elif self.algorithm == "dbscan":
                self.model = DBSCAN(**self.kwargs)
            else:
                raise ValueError(f"Unsupported algorithm: {self.algorithm}")
                
            labels = self.model.fit_predict(X_scaled)
            
            result = {
                "status": "success"
                "labels": labels
                "n_clusters": len(np.unique(labels)) if self.algorithm == "kmeans" else "variable"
                "inertia": getattr(self.model, 'inertia_', None)
                "metadata": {"rows_processed": len(clean_data), "algorithm": self.algorithm}
            }
            logger.info("Clustering completed successfully.")
            return result
        except Exception as e:
            logger.error(f"Clustering failed: {str(e)}")
            return {"status": "failed", "error": str(e)}

Pattern 3: BAD vs GOOD Implementation

# BAD: Hardcoded values, no validation, ignores scaling, bypasses error handling
def bad_clustering(data: pd.DataFrame) -> np.ndarray:
    model = KMeans(n_clusters=3)
    return model.fit_predict(data)

# GOOD: Validates input, scales data, checks convergence, returns structured results
def good_clustering(data: pd.DataFrame, n_clusters: int = 3) -> Dict[str, Any]:
    if data.empty or data.shape[0] < n_clusters:
        raise ValueError("Insufficient data for clustering")
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(data)
    model = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
    labels = model.fit_predict(X_scaled)
    return {"labels": labels, "inertia": model.inertia_, "scaler": scaler}

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Scikit-learn Clustering
• K-Means Guide — Scikit-learn
• DBSCAN Clustering — Scikit-learn docs
• Hierarchical Clustering — Scipy documentation
• Unsupervised Learning (Kaggle Learn)