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# DBSCAN clustering
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An implementation of DBSCAN clustering. Given a dataset, this can compute and return a clustering of that dataset.
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# Available Predicates
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* [dbscan/13](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Clustering/dbscan#dbscan13)
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---
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[links/resources](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Clustering/dbscan#connected-linksresources)
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## **_dbscan/13_**
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This is a one predicate model where you configure the model with the input parameters and get returned the results in the same predicate.
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```prolog
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%% part of the predicate definition
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dbscan( +float32, +integer, +integer,
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+string, +string,
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+pointer(float_array), +integer, +integer,
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-pointer(float_array), -integer,
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-pointer(float_array), -integer, -integer)
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```
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### Parameters
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| Name | Type | Description | Default |
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|------|------|-------------|---------|
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| epsilon | +float | Radius of each range search | 1.0 |
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| minPoints | +integer | Minimum number of points for a cluster | 5 |
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| batchMode | +integer(bool) | If true, all points are searched in batch. | (1)true |
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| selectionType | +string | If using point selection policy, the type of selection to use ("ordered", "random") | ordered |
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| treeType | +string | The type of tree to use ("kd", "r", "r_star", "x", "hilbert_r", "r_plus", "r_plus_plus", "cover", "ball") | kd |
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| data | +matrix | Input dataset to cluster | - |
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| assignments | -vector | Output matrix for assignments of each point. | - |
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| centroids | -matrix | Matrix to save output centroids to. | - |
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---
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# Connected Links/Resources
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If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
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* [MLpack::dbscan_C++\_documentation](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1dbscan_1_1DBSCAN.html)
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* [MLpack::dbscan_Python_documentation](https://www.mlpack.org/doc/stable/python_documentation.html#decision_stump)
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added some of the links from the python documentation
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* [Decision tree](https://www.mlpack.org/doc/stable/decision_tree)
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* [Decision stumps on Wikipedia](https://en.wikipedia.org/wiki/Decision_stump) |
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\ No newline at end of file |
