​

AI-Powered Clustering

AI-powered clustering in Leapfrog leverages quantitative research methods to group related data points on your canvas. Clusters are automatically formed by the AI model, containing 3 to many objects, based on the relationships and patterns detected in your research data. Once a cluster is created, you can use it to extract individual insights that guide your research analysis.

​

How AI-Powered Clustering Works

When you apply clustering on your canvas, the AI model examines the underlying data of each object—whether it’s quotes, themes, or other data nodes—and identifies patterns or similarities. The system then groups related items into clusters, helping you to visually understand relationships between your research data.

Each cluster is an organized set of data points, and the clustering process can reveal patterns you may not have noticed. It’s an essential feature for large datasets or when you’re seeking to gain insights at scale.

​

Annotating Clusters with AI

Once clusters are formed, the AI model goes a step further by annotating each cluster. These annotations help clarify why the specific groupings were made by the AI, offering transparency into the clustering process. It acts as a form of AI “explanation,” which can be useful in understanding how and why data has been grouped together.

This feature plays a critical role in demystifying AI behaviors and helps you build trust in the AI’s analysis by making the reasoning behind the clusters explicit.

​

Steps to Cluster Data

1. Select Data: Begin by selecting multiple data nodes on your canvas (e.g., quotes, notes, or imported data).
2. Click on “Cluster”: Use the Cluster option in the toolbar to trigger the AI model.
3. AI Creates Clusters: The AI will analyze the selected data points and group them into clusters.
4. View Annotations: Each cluster will be annotated by the AI, explaining why those data points were grouped together.

​

Using Clusters for Insight Extraction

After clusters have been formed, the next step is insight extraction. This process emulates the behavior of a human researcher, with the AI model generating conclusions or key takeaways from the clustered data. It is designed to go beyond just grouping related items by explaining why certain conclusions can be drawn from the data.

​

Steps to Extract Insights

1. Select a Cluster: Once clusters are formed on your canvas, choose a specific cluster that you want to analyze in detail.
2. Click on “Extract Insights”: From the menu, select the Extract Insights option.
3. AI Provides Explanation: The AI will generate insights, explaining why certain conclusions are drawn from that cluster. These insights are based on the patterns, relationships, and qualitative data within the cluster.

The insights generated aim to mimic the thought process of a human researcher, providing a deeper understanding of the data’s implications and offering explanations about the conclusions reached. The tool helps guide you through complex datasets and reduces the time spent manually interpreting data.

​

Why AI-Powered Clustering is Important

The combination of clustering and insight extraction enhances your ability to quickly identify patterns in qualitative research. By automatically grouping related data points and explaining those groupings, Leapfrog’s AI models help you:

• Save time: Reduce the manual work required to organize and interpret large datasets.
• Clarify AI actions: Understand the rationale behind AI-made decisions through cluster annotations.
• Derive actionable insights: Automatically generate meaningful conclusions from data clusters, speeding up your synthesis process.