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Building a Clustering Tool

Here we will demonstrate how to use the Artemis Planning Agent to build a command-line tool for clustering analysis in tabular data. The starting point is a simple Python template. Through an iterative process of question answering with our planner, we develop a specific and well-structured plan to build our desired tool with clear validation points and success criteria. The end result is a complete tool with data loading, multiple clustering algorithms, evaluation metrics, and visualization, along with comprehensive documentation.

Project Overview​

Goal: Build a CLI tool that clusters observations in CSV files using machine learning

Before: Python template (acc6e7a)

After: Complete clustering tool (e946245)

Target Users: Data scientists and analysts working with numeric datasets

Use Cases:

  • Customer segmentation and behavioral analysis
  • Gene expression clustering in bioinformatics
  • Document clustering for text analysis and organization

Planning Process​

We started with a simple statement: "I want to build a clustering tool."

The Artemis Planning Agent helped us think through the requirements by asking clarifying questions:

User Requirements:

  • What type of data? → Numerical/tabular data
  • How will users interact? → Command-line interface (CLI)
  • What file formats? → CSV only
  • Intended use case? → General-purpose (any tabular data)
  • What should it output? → Both CSV output and visualizations

Technical Requirements:

  • Which algorithms? → Multiple algorithms (K-means, DBSCAN, hierarchical clustering)
  • Include evaluation metrics? → Yes (silhouette score, inertia)
  • Data preprocessing? → Yes (automatic scaling, normalization, missing value handling)
  • Visualization library? → Seaborn (prettier static plots)
  • Save format? → PNG files
  • Configuration support? → Yes, support YAML/JSON config files

From these answers, Artemis generated a comprehensive plan with a clear strategy: Build a working MVP first, then enhance it with multiple algorithms and advanced features. This approach meant we'd have a functional tool using K-means after the core tasks, then add DBSCAN, hierarchical clustering, and comparison capabilities.

Each task in the plan comes with clear structure: specific goals and deliverables, detailed technical specifications, files to modify, and measurable success criteria. This transforms a vague request into actionable development steps with concrete validation points.

Implementation​

We built the tool in two phases: first creating a working MVP, then enhancing it with advanced features.

Phase 1: MVP - Working Clustering Tool (Tasks 1-7)​

These tasks created a complete, functional clustering tool:

1. Project setup and dependencies - Set up the foundation with pandas, scikit-learn, seaborn, and numpy for data processing, clustering algorithms, and visualization. (6400a9f)

2. Data loading module - Built a robust CSV data loader with comprehensive validation pipeline and error handling. (fb61df7)

3. Clustering algorithms module - Implemented K-means, DBSCAN, and hierarchical clustering with comprehensive validation and configurable parameters. (ee1cb38)

4. Cluster evaluation module - Added algorithm-aware cluster evaluation with silhouette score, inertia, and Davies-Bouldin index metrics. (71a9419)

5. Output generation - Implemented output generation and formatting module with CSV, metrics, and file management support. (35e6a03)

6. Configuration file support - Added configuration loading and validation module with YAML/JSON support, schema validation, and CLI overrides. (60d3ed5)

7. Data preprocessing module - Implemented comprehensive preprocessing pipeline with scaling, imputation, and column handling. (e190888)

Result after Task 7: A fully functional clustering tool with data loading, multiple algorithms, evaluation metrics, output generation, and configuration support.

Phase 2: Enhancement - Visualization, CLI, and Testing (Tasks 8-10)​

These tasks completed the tool with visualization and user interface:

8. Visualization module - Added comprehensive clustering visualization with PCA support, colorblind-friendly palettes, and edge case handling. (c5fc05a)

9. CLI interface with click - Built complete CLI frontend with algorithm execution pipeline, config file support, and comprehensive error handling. (7adb651)

10. Testing and documentation - Added comprehensive testing suite with pytest framework, unit/integration tests, usage guide, and algorithm documentation. (ff52068)

Final Result: A sophisticated clustering tool that supports K-means, DBSCAN, and hierarchical clustering with comprehensive evaluation metrics, visualization, configuration file support, CLI interface, and complete test coverage and documentation.

The Artemis Planning Agent breaks down requirements into manageable tasks and guides you through building each component step by step, from initial template to complete, documented clustering tool.

Last updated on by Paul Brookes