Clustering analysis using k-means

exploratory statistics

slug: recipe-exploratory-statistics-clustering-analysis-using-k-means

Recipe: Clustering analysis using k-means

category: exploratory statistics

Problem

You need to identify natural groupings in your data:

• segment customers based on behavior or demographics
• detect patterns or clusters in transactional or numeric data
• inform targeted marketing, promotions, or analysis

Solution

Follow these steps to perform k-means clustering:

• load the dataset
• select numeric fields or derived features for clustering
• apply k-means with a chosen number of clusters
• review cluster assignments and characteristics
• optionally visualize clusters

Step Sequence

load step -> [step-kmeans] -> calculate step -> chart step

Input Datasets

• transactions_clean — cleaned transactional data
• Notes: numeric fields like amount, frequency, recency, or other derived metrics

Output Dataset

• clustered_data — dataset with cluster assignments for each observation
• Notes: clusters can be used for segmentation, targeting, or further analysis

Step-By-Step Explanation

Step	Purpose	Notes
load step	Load dataset	Supports local file, database, or API sources
[step-kmeans]	Apply k-means clustering	Example: segment customers into 3–5 clusters based on purchase behavior
calculate step	Compute cluster statistics or derived metrics	Example: cluster centroids, average spend per cluster
chart step	Visualize clusters	Optional scatterplot, 2D/3D projection, or cluster distribution chart

Variations & Extensions

• Experiment with different numbers of clusters
• Preprocess data using calculate step or [step-standardize] for scaling
• Combine with classify step to assign new observations to existing clusters

Concepts Demonstrated

• Unsupervised clustering with k-means
• Data segmentation and pattern detection
• Integration of clustering results with analytics workflow
• Sequencing analytics and visualization steps

Related Recipes

• Time series analysis
• Regression analysis

Notes & Best Practices

• Standardize numeric features to prevent scale bias
• Evaluate cluster quality using silhouette scores or other metrics
• Document clustering parameters and rationale for reproducibility

Metadata

title: "Clustering analysis using k-means"
category: "exploratory statistics"
difficulty: "Intermediate"
tags: [clustering, k-means, segmentation, EDA]
inputs: [transactions_clean]
outputs: [clustered_data]
steps: [step-load, step-kmeans, step-calculate, step-chart]
author: "Tom Argiro"
last_updated: "2025-10-25"
doc_type: "recipe"