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28 lines
1.3 KiB
Markdown
28 lines
1.3 KiB
Markdown
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# DBSCAN clustering
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It is a density-based clustering algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.
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*(source: wikipedia)*
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### Constructor Parameters
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* $epsilon - epsilon, maximum distance between two samples for them to be considered as in the same neighborhood
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* $minSamples - number of samples in a neighborhood for a point to be considered as a core point (this includes the point itself)
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* $distanceMetric - Distance object, default Euclidean (see [distance documentation](math/distance/))
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```
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$dbscan = new DBSCAN($epsilon = 2, $minSamples = 3);
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$dbscan = new DBSCAN($epsilon = 2, $minSamples = 3, new Minkowski($lambda=4));
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```
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### Clustering
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To divide the samples into clusters simply use `cluster` method. It's return the `array` of clusters with samples inside.
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```
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$samples = [[1, 1], [8, 7], [1, 2], [7, 8], [2, 1], [8, 9]];
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$dbscan = new DBSCAN($epsilon = 2, $minSamples = 3);
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$dbscan->cluster($samples);
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// return [0=>[[1, 1], ...], 1=>[[8, 7], ...]]
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```
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