Why Use K-Means for Time Series Data? (Part Three)

The author uses K-Means clustering for anomaly detection on EKG data using InfluxDB and Chronograf. The process involves segmentation, windowing, clustering, reconstruction, normal error calculation, and anomaly detection. The author uses Python to perform these steps, including data exploration, segmentation, and reconstruction, and then detects anomalies in the EKG data by comparing the reconstructed data to the original data. The anomalies are then written to a database, where Kapacitor is used to set a threshold and alert on any errors that exceed the normal max reconstruction error of 8.8, which results in an alert being sent to the author via Slack when an anomaly is detected.

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