Automate Anomaly Detection with Medoid Clustering in n8n

This n8n workflow, specifically part 2 of a series, focuses on setting up medoids for anomaly detection using the Qdrant vector database. It automates the process of identifying two types of medoids within your dataset, crucial for effective clustering and outlier identification. By leveraging Qdrant's capabilities, the workflow retrieves cluster information, calculates the most representative point as a medoid using sparse matrix operations, and then updates the payload of these medoids in Qdrant. Furthermore, it extracts the medoid's vector and payload to establish a search threshold, identifying points furthest from the identified medoid, thus paving the way for precise anomaly detection. This workflow is essential for anyone looking to build sophisticated anomaly detection systems with automated data processing.