Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams

The number of Internet-of-Things (IoT) and edge devices has exploded in the last decade (IoT000; IoT00; AGG04), providing new opportunities to transform everyday people's lives. Coupled with advances in learning technologies (ML00; ML01), these can transform how people interact with their environment. A typical machine learning workflow in sensor-based applications starts with unlabeled data. That data is visualized, featurized, and clustered in search of patterns. Typically, labels are obtained, and subsequent sample-label pairs are used to train a classifier.