Differences
This shows you the differences between two versions of the page.
|
iothings:laboratoare:2025:lab7 [2025/11/06 18:30] dan.tudose [On-Device Light Anomaly Detection] |
iothings:laboratoare:2025:lab7 [2025/11/08 19:01] (current) dan.tudose [K-Means: IMU Activity Clustering] |
||
|---|---|---|---|
| Line 59: | Line 59: | ||
| ===== K-Means: IMU Activity Clustering ===== | ===== K-Means: IMU Activity Clustering ===== | ||
| - | K-means clustering is an unsupervised learning technique that groups data points into clusters based on how similar they are to one another. Unlike supervised methods that rely on labeled examples, K-means works with unlabeled data to reveal underlying patterns or structure. For instance, an wearable device might use K-means to discern between different activities, such as sleeping, walkinr or running. | + | K-means clustering is an unsupervised learning technique that groups data points into clusters based on how similar they are to one another. Unlike supervised methods that rely on labeled examples, K-means works with unlabeled data to reveal underlying patterns or structure. For instance, an wearable device might use K-means to discern between different activities, such as sleeping, walking or running. |
| + | |||
| + | {{ :iothings:laboratoare:2025:har.jpg?600 |}} | ||
| <note tip>You can learn more about K-means Clustering [[https://www.geeksforgeeks.org/machine-learning/k-means-clustering-introduction/|here]]!</note> | <note tip>You can learn more about K-means Clustering [[https://www.geeksforgeeks.org/machine-learning/k-means-clustering-introduction/|here]]!</note> | ||
