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- Collaboration with stakeholders is crucial for meaningful and useful clustering results.
- Unsupervised learning techniques like K-means are valuable for fraud risk detection when labeled data is unavailable.
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Real-world use cases for Camens clustering
This podcast episode explores the practical applications of Camens clustering and the importance of collaboration with stakeholders to ensure meaningful and useful clustering results. It emphasizes the need for unsupervised learning techniques when labeled data is not available, as illustrated by a fraud risk scoring project in a banking environment.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode