Luma

Affinity Clustering & Analysis

Affinity Clustering organizes raw data into themes. After research, you might feel swamped. This exercise transforms chaos into clarity. Teams group similar items. Patterns emerge. Insights crystallize. It's a hands-on way to make sense of complexity. I've found it works best when you resist pre-conceived notions.

Duration
1 hour
Group Size
4-12
Category
Luma
Difficulty
Easy
Energy
Medium

Objectives

Participants will: transform data into themes; identify insights; build shared understanding; prepare findings; facilitate discussions; reveal connections.

Outcomes

You'll get: clustered affinity data; organized insights; a foundation for synthesis.

Step-by-Step Instructions


  1. Preparation (15 minutes): Capture observations on sticky notes (one thought per note). Use consistent formatting. Color-code if needed. 2. Silent Clustering (20-30 minutes): Place notes randomly on a wall. Sort silently. Move similar notes together. Let patterns emerge. Continue until movement slows. 3. Refinement Phase (20 minutes): Discuss emerging groups. Create subgroups if needed. Identify relationships between clusters. Name clusters (but not too early!). Document the final arrangement.

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Facilitator Tips


  • Start silently. This prevents domination. - If using after "Visualize the Vote", use top voted elements as starting points. - Resist early labeling. Clusters shift. - Look for outliers. They may reveal insights. - Use a "parking lot" for items that don't fit. - Time-box discussions. Keep energy high.

Pre-Work

For Facilitators

  • Review participant profiles and expectations
  • Prepare all materials and supplies
  • Test technology and room setup

For Participants

  • Complete pre-session survey
  • Review background materials
  • Prepare examples or case studies

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Materials Required


  • Sticky notes (various colors)

  • Large wall space (at least 8' x 4')

  • Thick markers

  • Labels

  • String/tape

  • Camera

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Resources & Templates

Affinity Clustering template
A way to sort items according to similarity

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