[chernoff-basic] Chernoff Faces for Multivariate Data

[MarkusNeusinger]

Chernoff faces map multivariate data to facial features (eyes, mouth, nose, face shape), leveraging human face recognition ability for pattern detection across observations. Each data point becomes a face whose features represent variable values.

[claude]

✅ Spec ID: chernoff-basic

Rationale: Chernoff faces are a unique multivariate visualization technique that maps data dimensions to facial features. This is a new plot type with no existing duplicate in the repository.

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[github-actions]

✅ Specification Ready: chernoff-basic

PR: #3008

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specification.md

chernoff-basic: Chernoff Faces for Multivariate Data

Description

Chernoff faces visualize multivariate data by mapping each variable to a facial feature (eye size, mouth curvature, face width, nose length, etc.), transforming each observation into a unique cartoon face. This technique leverages humans' innate ability to recognize and distinguish faces, making it easier to identify patterns, clusters, and outliers across multiple dimensions simultaneously.

Applications

• Comparing financial health indicators across companies where each face represents a company's performance metrics
• Visualizing patient health profiles in medical research where facial features encode vital signs and lab results
• Analyzing customer segments in marketing where each face represents a customer profile with demographic and behavioral attributes

Data

• observation_id (categorical) - Unique identifier for each observation/face
• variable_1 through variable_n (numeric) - Continuous variables mapped to facial features (typically 5-15 variables)
• Size: 5-50 observations (faces become hard to compare with too many)
• Example: Iris dataset with 4 measurements per flower, or car ratings with multiple performance metrics

Notes

• Each variable should be normalized to a common scale (0-1) before mapping to facial features
• Common feature mappings: face width, face height, eye size, eye spacing, eyebrow slant, nose length, mouth curvature, mouth width
• Grid layout recommended for comparing multiple faces side by side
• Consider colorizing faces by group membership if categorical grouping exists

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[github-actions]

🚀 Specification Merged!

Specification: chernoff-basic
PR: #3008 (merged)

The specification is now in main and synced to PostgreSQL.

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Available libraries: matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, highcharts, letsplot

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