# kddbscan-rs

Rust implementation of the kddbscan clustering algorithm.

From the authors of kDDBSCAN algorithm.

Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varied densities. To solve the problem, this paper extends DBSCAN by exploiting a new density definition and proposes a novel algorithm called k -deviation density based DBSCAN (kDDBSCAN). Various datasets containing clusters with arbitrary shapes and different or varied densities are used to demonstrate the performance and investigate the feasibility and practicality of kDDBSCAN. The results show that kDDBSCAN performs better than DBSCAN.

## Installation

Add `kddbscan`

as a dependency in your `Cargo.toml`

file

```
[dependencies]
kddbscan = "0.1.0"
```

## Usage

Implement `IntoPoint`

trait on your point struct. And pass a vector of points to the `cluster`

function.

```
use kddbscan::{cluster, IntoPoint, ClusterId};
pub struct Coordinate {
pub x: f64,
pub y: f64,
}
impl IntoPoint for Coordinate {
fn get_distance(&self, neighbor: &Coordinate) -> f64 {
((self.x - neighbor.x).powi(2) + (self.y - neighbor.y).powi(2)).powf(0.5)
}
}
fn main() {
let mut coordinates: Vec<Coordinate> = vec![];
coordinates.push(Coordinate { x: 11.0, y: 12.0 });
coordinates.push(Coordinate { x: 0.0, y: 0.0 });
coordinates.push(Coordinate { x: 12.0, y: 11.0 });
coordinates.push(Coordinate { x: 11.0, y: 9.0 });
coordinates.push(Coordinate { x: 10.0, y: 8.0 });
coordinates.push(Coordinate { x: 1.0, y: 2.0 });
coordinates.push(Coordinate { x: 3.0, y: 1.0 });
coordinates.push(Coordinate { x: 4.0, y: 4.0 });
coordinates.push(Coordinate { x: 9.0, y: 0.0 });
let clustered = cluster(coordinates, 2, None, None);
}
```

## Showcase

This is the output of example project.

## Contribution

All PRs and issues are welcome. and starts are also welcome.

## License

This project is under the MIT license and the algorithm is under the CC BY 4.0 license.