# [−][src]Crate kddbscan

A k -Deviation Density Based Clustering Algorithm (kDDBSCAN)

Research Paper

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 the following to your `Cargo.toml` file:

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

# Usage

```use kddbscan::{cluster, IntoPoint};

pub struct Coordinate {
pub x: f64,
pub y: f64,
}

// Implement IntoPoint trait to your data structur
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() {
// Create a vector with your data
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: 11.0 });
coordinates.push(Coordinate { x: 1.0, y: 2.0 });
coordinates.push(Coordinate { x: 3.0, y: 1.0 });

// Call cluster function
let clustered =  cluster(coordinates, 2, None, None);
let first_cluster_id = clustered.get(0).unwrap().get_cluster_id();
let second_cluster_id = clustered.get(1).unwrap().get_cluster_id();

assert_eq!(first_cluster_id, clustered.get(2).unwrap().get_cluster_id());
assert_eq!(first_cluster_id, clustered.get(3).unwrap().get_cluster_id());
assert_eq!(second_cluster_id, clustered.get(4).unwrap().get_cluster_id());
assert_eq!(second_cluster_id, clustered.get(5).unwrap().get_cluster_id());
}```

## Structs

 PointWrapper This struct is using to store temporary values for points such as cluster_id and index of the point

## Enums

 ClusterId Cluster id types

## Traits

 IntoPoint You should implement `IntoPoint` for all points

## Functions

 cluster Clustering a vec of structs