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| en:iot-reloaded:dbscan [2024/12/10 16:05] – blanka | en:iot-reloaded:dbscan [2024/12/10 20:44] (current) – pczekalski | ||
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| One of the essential concepts is the point' | One of the essential concepts is the point' | ||
| - | <figure Point' | ||
| {{ : | {{ : | ||
| - | < | ||
| - | </ | ||
| where: | where: | ||
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| * Points that are not core and are not reachable from any core point are considered noise or outliers. | * Points that are not core and are not reachable from any core point are considered noise or outliers. | ||
| - | < | + | < |
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| * If it is a core point, form a cluster by grouping it with all directly density-reachable points. | * If it is a core point, form a cluster by grouping it with all directly density-reachable points. | ||
| * Move to the next unvisited point and return to step 1. | * Move to the next unvisited point and return to step 1. | ||
| - | * Border points are added to the nearest cluster, and points | + | * Border points are added to the nearest cluster, and points not reachable from any core point are marked as noise. |
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| * It struggles with clusters of varying densities since eps is fixed. | * It struggles with clusters of varying densities since eps is fixed. | ||
| - | DBSCAN is great for discovering clusters in data with noise, especially when clusters are not circular or spherical. | + | DBSCAN is excellent |
| - | Some application examples: | + | Some application examples |
| - | < | + | < |
| {{ : | {{ : | ||
| < | < | ||
| </ | </ | ||
| - | < | + | < |
| {{ : | {{ : | ||
| < | < | ||
| </ | </ | ||
| - | A typical application in signal processing: | + | A typical application in signal processing |
| - | < | + | < |
| {{ : | {{ : | ||
| < | < | ||
